AI for the ERP you already run
Private, grounded AI on Infor, SAP, Oracle, Microsoft Dynamics, Epicor, IFS, Deltek and the specialist systems that run manufacturing, aerospace, defense and electronics plants. Each guide names the modules, APIs and regulations involved, and is honest about when the vendor's own copilot is enough.
Most of this work runs on-prem or in a sovereign cloud, because ERP data is the business: pricing, costs, suppliers, drawings, and often export-controlled technical data.
AI for Infor
42 guidesSyteLine / CloudSuite Industrial, LN, M3, VISUAL, XA, and legacy Baan, via ION, IDO, and direct database paths.
AI for Infor SyteLine and CloudSuite Industrial
Infor SyteLine and CloudSuite Industrial hold the order, inventory, and routing data that planners and CSRs look up dozens of times a day; a private AI layer answers those questions in plain language, drafts routine paperwork, and flags MRP exceptions without sending ERP data to a public model API. On-prem deployment keeps the SQL Server database, IDO traffic, and ION messages inside the plant network, and every write-back still goes through the same approval a person would give today.
Read the guideCSI, kept on-premOn-Prem AI for CloudSuite Industrial, Without the Multi-Tenant Cloud Move
CloudSuite Industrial shops that run on-premise, whether by choice or by contract, still want the natural-language and agent capabilities showing up in Infor's cloud messaging, without moving their production database or their ERP data into Infor's multi-tenant environment. An on-prem private LLM, connected through the same IDO and ION paths a normal integration would use, delivers most of that value while the ERP itself stays exactly where it is.
Read the guideInfor LN + AIAI for Infor LN: Sessions, BODs, and Engineer-to-Order Work
Infor LN's session-based structure and Business Object Document (BOD) integration layer already give you a supported way to read and write ERP data; a private AI layer uses that same path to answer questions in plain language, summarize project and engineer-to-order status, and draft routine documentation, without sending LN data to a public model API. On-prem or private-cloud deployment keeps the AI workload inside the same boundary LN itself already runs in.
Read the guideInfor M3 + AIInfor M3 AI: A Practical Path from MI Programs to a Private Assistant
Infor M3's strength in distribution, fashion, food and beverage, and equipment manufacturing rests on a dense set of MI programs and the H5 user interface that most staff never fully master; a private AI layer answers questions in plain language by calling the same MI programs and MEC integration paths your existing integrations already use, without sending M3 data outside your own infrastructure. For CIOs across Europe, on-prem or EU-region private cloud deployment also settles the GDPR data-residency question before it is asked.
Read the guideSyteLine + air-gapped AIAn Air-Gapped Private LLM for SyteLine, Built for Defense Suppliers
Defense suppliers running Infor SyteLine on a network segmented for ITAR or CMMC reasons need AI that never depends on internet access at inference time, not a cloud AI feature with a compliance disclaimer attached. An air-gapped deployment runs the model, the retrieval index, and the SyteLine connector entirely inside the controlled network, with model and software updates applied as offline packages on a schedule you control.
Read the guideInfor VISUAL + on-prem AIAI for Infor VISUAL ERP: Answers and Agents for Job Shops
Infor VISUAL runs on a SQL Server database with a documented schema and an API Toolkit, which makes it a reasonable target for grounded natural-language question answering and light automation without touching the core application. On-prem AI reads job, routing, estimate, and inventory tables directly, so a planner or estimator gets an answer in seconds instead of running a saved report or waiting on a scheduler. The right entry point is usually quoting turnaround and job status visibility, both of which are read-only and low-risk to pilot.
Read the guideInfor XA on IBM i + on-prem AIAI for Infor XA on IBM i: On-Prem, No Replatforming Required
Infor XA still runs core manufacturing for a meaningful number of shops on IBM i, and none of them need to replatform to get value from AI. DB2 for i is a mature, well-understood database with SQL access, ODBC drivers, and years of tooling built around it, which is exactly what a retrieval-augmented question-answering system needs. The practical path is a connector that reads DB2 for i views on a schedule or via change data capture, paired with a model running on a small on-prem GPU server, so nothing about your XA environment has to change.
Read the guideInfor OS + on-prem AI agentsAI Agents Over Infor ION API, BODs, and the Infor Data Lake
Infor ION already gives you a documented, event-driven integration backbone across CloudSuite applications: ION API Gateway, Business Object Documents (BODs), workflows, and the Infor Data Lake for analytics. That backbone is a strong foundation for AI agents, because agents need exactly what ION already provides: structured events, a consistent object model, and an audit trail. This is complementary to Infor's own GenAI and Coleman capabilities, not a replacement for them, and the choice between the two usually comes down to where you want the model and the data to physically sit.
Read the guideBaan legacy + on-prem AIAI for Legacy Baan IV/V: Capture the Knowledge Before It Walks Out the Door
Baan IV and Baan V installations still run manufacturing operations across Europe, typically supported by a shrinking group of specialists who know the 4GL sessions, tables, and undocumented customisations by memory rather than by documentation. AI grounded on the Baan database and on 4GL source can capture that knowledge before it retires with the people who hold it, answer routine questions without a Baan specialist, and produce documentation that materially reduces risk and cost in an eventual migration to LN, CloudSuite, or another platform.
Read the guideInfor LN + on-prem AI for A&DAI for Infor LN in Aerospace and Defense Manufacturing
Infor LN's project and engineer-to-order structures, contract management, and configuration control make it a common fit for aerospace and defense manufacturers, and those same structures give AI a well-defined foundation to work from: sessions, tables, and BODs that already reflect how contracts, serialized items, and configuration baselines are managed. The requirement that changes everything for this audience is control of ITAR-controlled technical data, which is why deployment has to run inside your network, not through a public model API.
Read the guideInfor AI Buyer GuideWhat an Infor AI Consulting Partner Should Actually Deliver
An Infor AI consulting engagement should produce a working, grounded question-answering or agent capability over your specific SyteLine, LN, or M3 instance, built on ION API, IDOs, or the Data Lake, with a clear read-only-first rollout and a defined relationship to Infor's own Coleman AI roadmap. This guide covers what to demand from any Infor-focused AI partner, without claiming Infor partner status for Netray or anyone else.
Read the guideInfor LX + AIAI for Infor LX: A Private Layer on Top of Your IBM i Investment
Infor LX, the direct descendant of BPCS, still runs core manufacturing on IBM i at plants that have no near-term plan to replatform; a private AI layer reads LX data through Business Object Documents and IBM i file access, answers planner and CSR questions in plain language, and drafts routine paperwork without sending green-screen data to a public model API. On-prem deployment keeps the AI workload inside the same IBM i boundary LX itself already runs in.
Read the guideInfor System21 + AIAI for Infor System21: Grounded Answers on Top of IBM i
Infor System21 has run distribution and manufacturing operations on IBM i for decades, and its Aurora web interface modernized the front end without changing what makes the system hard for casual users: knowing which of its many programs and inquiries holds the answer. A private AI layer connects to System21's supported integration paths, IBM i database access, and BODs where ION is in use, to answer questions in plain language and draft routine documents without sending distribution data to a public model API.
Read the guideCloudSuite Distribution (SX.e) + AIAI for Infor CloudSuite Distribution: Answers Grounded in SX.e Data
Infor CloudSuite Distribution, built on the SX.e platform and its Progress OpenEdge database, runs order management, purchasing, and rebate processing for wholesale distributors who juggle high order volume and thin margins on every line. A private AI layer answers order, inventory, and rebate questions in plain language by reading SX.e's structured data and EDI traffic, without sending pricing or customer data to a public model API, and keeps any write-back behind the same approval a buyer or CSR would give today.
Read the guideDistribution FACTS + AIAI for Infor Distribution FACTS, Without Replacing the System
Infor Distribution FACTS runs order entry, purchasing, and inventory for small and mid-size wholesale distributors on IBM i, and it does that job reliably at a scale where a full ERP replacement rarely pencils out. A private AI layer adds plain-language question answering and drafting on top of FACTS's existing data, so an owner or ops lead gets AI value without a system change, and without customer or pricing data ever leaving the business's own network.
Read the guideSyteLine + medical devicesAI on Infor SyteLine for Medical Device Manufacturers: Genealogy, DHR and Audit Evidence
AI on Infor SyteLine for medical device manufacturers connects a private model to SyteLine lot, serial, job and purchasing data so quality and operations staff can trace a component to every finished device, assemble device history record evidence, and answer auditor questions in minutes. It runs on-prem or in a private cloud, is read-only by default, and leaves the quality system of record, and every decision, with your people.
Read the guideSyteLine + injection moldingAI on Infor SyteLine for Plastics Injection Molders: Presses, Molds, Resin and Scrap
AI on Infor SyteLine for injection molders connects a private model to SyteLine job, routing, item and inventory data so planners and plant managers can ask about press loading, mold availability, resin lots and scrap variance in plain English. It runs on-prem or in a private cloud, shows the query behind each answer, and does not change schedules or records without human approval.
Read the guideSyteLine + metal fabricationAI on Infor SyteLine for Metal Fabricators: Quotes, Heat Lots, Routings and Job Cost
AI on Infor SyteLine for metal fabricators connects a private model to SyteLine estimates, jobs, routings, heat and lot data and purchasing so estimators and operations managers can find comparable past jobs, trace material to mill certs, and explain job cost variance in plain English. It runs on-prem or privately, shows each query, and drafts without writing to SyteLine unless a person approves.
Read the guideSyteLine + industrial machineryAI on Infor SyteLine for Industrial Machinery Makers: Configured Orders, Projects and Service Parts
AI on Infor SyteLine for industrial machinery manufacturers connects a private model to SyteLine orders, configured BOMs, projects, purchasing and service data so sales engineers, planners and service staff can ask about order status, long-lead parts, project cost and installed-base spares in plain English. It runs on-prem or in a private cloud, shows its queries, and drafts rather than posts.
Read the guideSyteLine + food and beverageAI on Infor SyteLine for Food and Beverage Plants: Lot Trace, Expiry, Yield and Recall Readiness
AI on Infor SyteLine for food and beverage manufacturers connects a private model to SyteLine lot, job, purchasing and shipment data so food safety, quality and planning staff can run mock recalls, check expiry exposure and explain yield variance in plain English. It runs on-prem or in a private cloud and is read-only by default, with people approving any action.
Read the guideSyteLine + furnitureAI on Infor SyteLine for Furniture Manufacturers
AI on SyteLine for furniture manufacturers answers questions over customer orders, configured BOMs, job routings and lot records in plain language, and automates the repetitive work around finishes, fabrics and dealer order status. It reads SyteLine through IDOs with the user's own role, shows the query behind each answer, and can run on the customer's own servers.
Read the guideSyteLine + oil and gasAI on Infor SyteLine for Oil and Gas Equipment Manufacturers
Oil and gas equipment makers on SyteLine run engineered-to-order work with heavy traceability: heat lots, material test reports, serial-numbered assemblies and API-driven quality records. AI on SyteLine answers questions across projects, jobs, lots and serials in plain language, retrieves the supporting documents, and can run entirely on the customer's own infrastructure.
Read the guideSyteLine + automotiveAI on Infor SyteLine for Automotive Suppliers
Automotive suppliers on SyteLine live on EDI releases, tight delivery windows and IATF 16949 records. AI on SyteLine answers questions across releases, shipments, jobs and quality data in plain language, explains why a release will be missed, and retrieves PPAP and audit evidence, with read-only access by default and an on-prem option.
Read the guideSyteLine + packagingAI on Infor SyteLine for Packaging Manufacturers
Packaging manufacturers on SyteLine run high-mix, short-run work with substrate, ink and tooling that vary by job and repeat orders that must match last time. AI on SyteLine answers questions about job cost, repeat specifications, material coverage and food-contact records in plain language, reading ERP data with the user's own permissions and running on the customer's infrastructure if required.
Read the guideSyteLine + HVAC equipmentAI on Infor SyteLine for HVAC Equipment Manufacturers
HVAC equipment makers on SyteLine configure units to order, track serial-numbered products through warranty, and work within efficiency and refrigerant regulations. AI on SyteLine answers questions about configured orders, coil and compressor supply, serial history and submittals in plain language, with read-only access by default and the option to run on the customer's own infrastructure.
Read the guideInfor LN + ETO machineryInfor LN AI for Engineer-to-Order Industrial Machinery Builders
AI for Infor LN in engineer-to-order machinery means a private model that reads project, engineering BOM, purchasing and shop floor data and answers questions such as which projects are slipping because of a late long-lead part, or what an engineering change does to committed cost. It runs on-prem next to LN, is read-only by default and shows the query behind every answer.
Read the guideInfor LN + electronicsInfor LN AI for High-Tech Electronics: BOMs, Shortages and Traceability
AI on Infor LN for high-tech electronics answers component-level questions that spreadsheets usually carry: which products are exposed to an obsolete part, which open orders a shortage blocks, whether a substance declaration is complete, and which serials contain a suspect lot. It runs privately next to LN, reads live data and shows its queries.
Read the guideInfor M3 + food and beverageInfor M3 AI for Food and Beverage: Traceability, Yield and Shelf Life
AI for Infor M3 in food and beverage gives quality, planning and plant teams plain-language access to lot genealogy, remaining shelf life, yield variance and allergen data held in M3, with the query shown. It runs privately next to M3, so a recall trace or recipe cost question is answered in minutes without sending formulations to an outside model.
Read the guideInfor M3 + fashionInfor M3 AI for Fashion and Apparel: Seasons, Sizes and Allocation
AI on Infor M3 for fashion and apparel lets merchandisers, planners and sourcing teams ask plain-language questions across the style, color and size matrix, season buys, supplier purchase orders and customer allocations. It runs privately beside M3, reads live data, shows its queries and keeps collection and cost data inside your boundary.
Read the guideInfor M3 + chemicalsInfor M3 AI for Chemical Manufacturers: Formulas, Batches and Safety Data
AI on Infor M3 for chemicals gives plant, quality and regulatory staff plain-language access to formula, batch genealogy, potency-adjusted consumption, co-product yield and hazard data linked to M3 items. It runs privately beside M3, shows the query behind each answer and keeps proprietary formulations on your own infrastructure.
Read the guideSyteLine purchasing + AIAI for SyteLine Purchasing: Open POs, Late Vendors and Planned Order Release
AI for SyteLine purchasing connects a private model to the purchase order, vendor and item-vendor data so buyers can ask which POs are late, which planned orders to release and which vendors keep missing promise dates. Answers come from live SyteLine data with the query shown, and any change to a PO stays a human-approved action.
Read the guideSyteLine planning + APS AIAI for SyteLine Production Planning and Scheduling (MRP and APS)
AI for SyteLine planning lets planners ask why a job is late, which work center is the constraint and which MRP messages deserve action, with answers grounded in jobs, routings, work centers and the APS schedule. It explains and recommends; the schedule and release decisions remain with the planner.
Read the guideSyteLine quality + AIAI for SyteLine Quality: Nonconformance Reports and Inspection Data
AI for SyteLine quality reads nonconformance, inspection and receiving data so quality engineers can ask which defects repeat, which suppliers drive rejects and which open NCRs are aging. It drafts summaries and disposition suggestions for review; the formal disposition and CAPA approval remain human decisions in your quality process.
Read the guideSyteLine inventory + AIAI for SyteLine Inventory Control and Cycle Counting
AI for SyteLine inventory reads on-hand, location, lot and transaction data to suggest which items to cycle count, explain count variances and find negative, stale or misplaced stock. Adjustments are proposed for approval and posted in SyteLine under your normal controls.
Read the guideSyteLine finance + AIAI for SyteLine Finance: Month-End Close, Reconciliation and Variance Review
AI for SyteLine month-end close helps controllers find unposted transactions, reconcile subledgers such as inventory and WIP to the general ledger, and explain period variances in plain language. It prepares evidence and drafts commentary; postings, journals and period close remain controlled actions in SyteLine.
Read the guideSyteLine quoting + private AIAI for SyteLine Sales Quoting and Estimating That Learns From Your Own Won and Lost Jobs
AI for SyteLine quoting reads estimate, customer order and job history through IDOs or read-only SQL, finds comparable past work, and drafts a cost build-up with margin and lead-time checks for an estimator to approve. Nothing is written to the quote or customer order without a human clicking approve, and the model can run on the customer's own GPUs.
Read the guideSyteLine service + private AIAI for SyteLine Service Management and Field Service Without Replacing Your Service Module
AI for SyteLine service reads service requests, installed units, contracts, warranty terms and parts history to help dispatchers triage incidents, check coverage and prepare technicians. It drafts and recommends, and a person approves anything written back to the service order. It can run entirely on the customer's own servers.
Read the guideInfor LN procurement + AIAI for Infor LN Purchasing: Faster Exception Handling on Orders, Schedules and Suppliers
AI for Infor LN purchasing reads purchase orders, schedules, contracts, receipts and supplier data through ION APIs or read-only SQL, and helps buyers triage late deliveries, price variances and expedite decisions. Drafted actions need buyer approval before anything is written back, and the model can run on-prem.
Read the guideInfor LN PCS + private AIAI for Infor LN Project Control: Budget, Cost and Forecast Questions Answered From Your PCS Data
AI for Infor LN Project Control reads project budgets, estimates, planned and actual cost, hours and commitments through ION or read-only SQL, then answers variance and forecast questions in plain language. Forecasts are drafts for a project controller to review, and the model can run on-prem for controlled programs.
Read the guideInfor LN warehousing + AIAI for Infor LN Warehousing: Inventory, Inbound and Outbound Exceptions Made Searchable
AI for Infor LN Warehousing reads warehouse orders, inbound and outbound lines, locations, handling units and inventory transactions to explain discrepancies and surface exceptions in plain language. It drafts adjustments and tasks for approval, never moves stock on its own, and can run on-prem next to LN.
Read the guideInfor M3 + planning AIInfor M3 Supply Chain Planning With AI That Explains the MRP Output
AI for Infor M3 supply chain planning reads item, warehouse balance, planning order and purchase order data through M3 APIs and answers planner questions such as why a shortage exists and which supplier or work order is driving it. It does not replace the M3 planning engine; it explains and triages its output, and any change to a planned order or purchase order stays behind human approval.
Read the guideInfor M3 + order AIInfor M3 Customer Order Management With AI for Status, Allocation and Promise Dates
AI for Infor M3 customer order management lets customer service and sales operations ask where an order stands, why a line is not allocated and what is blocking delivery, with answers built from live order head, order line, stock and purchase data. It can also draft customer updates for human approval, while order entry and changes remain in M3.
Read the guideAI for SAP
27 guidesS/4HANA, ECC 6.0, Business One, and ByDesign, via OData, BAPI/RFC, IDoc, CDS views, and BTP.
AI for SAP S/4HANA, Running On-Prem or in Your Private Cloud
Adding AI to an on-prem or private-cloud SAP S/4HANA system means running an open-weight model next to HANA, grounding it in the CDS views and OData services your Basis team already exposes, and keeping every prompt and completion inside your own network boundary. The pattern is read-only by default: the model answers questions and drafts documents, and a person approves anything that writes back through a BAPI or IDoc. This fits S/4HANA shops that cannot, or will not, send MM, SD, or PP data to a public LLM API.
Read the guideSAP ECC 6.0 + AIGet AI Value From SAP ECC Now, and Use It to De-Risk the Move to S/4HANA
SAP ECC 6.0 customers do not have to wait for an S/4HANA go-live to get generative AI value: a private LLM can ground itself in ECC's existing BAPIs, IDocs, and ABAP tables today, and the same discovery work becomes evidence for the S/4HANA migration itself. With mainstream maintenance for ECC 6.0 set to end in 2027, most IT organizations are deciding whether to convert, re-implement, or delay, and AI can help answer that question while reducing the manual effort of cutover.
Read the guideSAP Business One + AIAI for SAP Business One, Without Sending Your Books to a Public API
SAP Business One's Service Layer and DI API already expose most of what a small manufacturer needs for AI: sales orders, production orders, item master, and financials, all through a documented interface. A private LLM grounded in that data, running on a modest on-prem server or a single private-cloud GPU instance, can answer questions and draft documents without the owner sending customer and financial data to a public AI service.
Read the guideSAP Joule + on-prem alternativeSAP Joule or a Private LLM Beside SAP: An Honest Comparison
SAP Joule and SAP Business AI are strong choices when your S/4HANA landscape is already cloud-connected and your data governance allows it; a private, self-hosted LLM beside SAP makes more sense when data cannot leave your network, you need to reach systems Joule doesn't cover such as ECC or Business One, or you want the retrieval logic and model choice under your own control. Most CIOs we talk to end up running both, not choosing one over the other.
Read the guideSAP BTP + self-hosted AISAP BTP and a Private LLM: Where the Generative AI Hub Fits and Where It Doesn't
SAP BTP's Generative AI Hub gives enterprise architects a managed way to call various models through a consistent interface inside the SAP ecosystem, using CAP, Integration Suite, and SAP's extensibility model. A self-hosted LLM outside BTP is the right choice when the model, the data, or both need to stay entirely within customer-controlled infrastructure rather than SAP-managed cloud infrastructure, even a well-governed one.
Read the guideSAP + on-prem AI for A&DAI on SAP for Aerospace and Defense Manufacturers, Without the ITAR Exposure
Aerospace and defense manufacturers running SAP S/4HANA or ECC can add natural-language search, configuration control assistance, and quote drafting without sending ITAR-controlled technical data to a public model API. The pattern is a private or air-gapped LLM grounded on SAP tables through read-only OData or RFC connectors, with every generated answer traceable back to source records and every write action gated behind human approval.
Read the guideSAP PP/MRP + AI agentsAI Agents for SAP Production Planning, MRP, and Exception Triage
A planner running SAP MRP typically opens MD04 to a wall of exception messages with no ranking beyond the default sort order. An AI agent grounded on the same MRP data can group and prioritize those exceptions, explain a predictive MRP shortage in plain language, and draft the planned-order or purchase-requisition action for the planner to approve, without ever changing an order on its own.
Read the guideSAP QM + on-prem AIAI for SAP Quality Management: Notifications, 8D, and CAPA Without the Manual Drafting
Quality teams running SAP QM spend a disproportionate share of their time on manual work that is really document assembly: triaging notifications, writing the first draft of an 8D or CAPA report, summarizing inspection results for a management review. An AI layer grounded on QM01/QA32 data can draft that first pass from real notification and inspection records, leaving the root-cause judgment to the quality engineer who signs the report.
Read the guideSAP PM/EAM + on-prem AIAI for SAP Plant Maintenance: Notifications, Work Orders, and Predictive Signals
Maintenance teams on SAP PM lose time to two related problems: notifications written in inconsistent free text that make reliability analysis unreliable, and work order drafting that repeats the same manual steps every time. An AI layer grounded on your own PM data can classify notifications with consistent failure codes, draft the work order text and operations, and correlate predictive maintenance signals with recent notification history, all as drafts a planner or technician confirms.
Read the guideSAP ByDesign + private AIAI Integration for SAP Business ByDesign, Built on OData
SAP Business ByDesign is a multi-tenant SaaS ERP, which means you cannot self-host the ERP itself, but you can build an AI layer that reads ByDesign through its OData and SOAP communication scenarios and runs on infrastructure you control. For a mid-market finance team, that means natural-language reporting, variance commentary drafting, and document search without financial data ever reaching a public model API.
Read the guideSAP AI Buyer GuideSAP AI Consulting for On-Prem and Private Deployments
SAP AI consulting for on-prem or private-cloud customers needs to answer one question honestly: does the proposed architecture keep S/4HANA or ECC data inside your boundary, or does it depend on SAP Business AI, Joule, or a third-party model API that your compliance posture rules out. This guide covers the OData, BAPI/RFC, and CDS mechanics a serious proposal should reference, and the questions to ask any SAP-focused AI partner before committing.
Read the guideSAP EWM + S/4HANA + private AIAI for SAP EWM: Ask About Warehouse Tasks, Handling Units and Stock in Plain English
AI for SAP EWM connects a privately hosted model to EWM's warehouse tasks, warehouse orders, handling units, storage bins and quants, through OData or CDS-based APIs, BAPIs and the warehouse monitor data, and joins them with S/4HANA deliveries and production orders. Users get plain-English answers with the source query shown, while the EWM transactions remain the system of record.
Read the guideSAP B1 + food and beverageAI on SAP Business One for Food and Beverage Manufacturers: Batch, Expiry and Recall Answers in Seconds
AI for SAP Business One in a food plant connects a private language model to the Service Layer and read-only SQL views so quality, planning and purchasing staff can ask about batches, expiry dates, recipe costs and supplier lots in plain English. Answers are grounded in live OBTN, OITW, OWOR and OITT data, show the query used, and run on-prem so recipes and customer pricing stay inside the plant network.
Read the guideSAP B1 + chemicalsAI on SAP Business One for Chemical Manufacturers: Formulas, Batches and Safety Data in One Place
AI for SAP Business One in a chemical company connects a private model to batch, formula and inventory data so formulators, QA and logistics can ask about lot attributes, formula costs, hazard classes and expiry in plain English. It reads Business One through the Service Layer and read-only views, retrieves SDS and REACH documents, and runs on-prem so formulations stay confidential.
Read the guideSAP B1 + electronicsAI on SAP Business One for Electronics Manufacturers: BOMs, Shortages and Traceability
AI for SAP Business One in an electronics company connects a private model to BOMs, inventory, purchase orders and serial or lot records so planners, buyers and quality engineers can ask about shortages, where-used, cost and traceability in plain English. It reads Business One via the Service Layer and read-only views, runs on-prem, and keeps customer designs and pricing inside your network.
Read the guideSAP B1 + machinery buildersAI on SAP Business One for Industrial Machinery Builders: Projects, Spares and Service in One Conversation
AI for SAP Business One in a machinery company connects a private model to project, BOM, purchasing, serial and service data so project managers, service coordinators and parts staff can ask for build status, spare part history and warranty answers in plain English. It runs on-prem, reads through the Service Layer and read-only views, and cites every document it uses.
Read the guideSAP B1 + medical devicesAI on SAP Business One for Medical Device Manufacturers: Traceability and Records Retrieval Without Touching the Validated State
AI for SAP Business One in a medical device company is a read-only retrieval layer over lot, serial, supplier, production and shipment records. It helps quality, regulatory and operations staff find device history, UDI data and supplier status in plain English, runs on-prem, and does not write to records or replace validated quality system processes.
Read the guideS/4HANA + automotive supplyAI for SAP S/4HANA in Automotive: Scheduling Agreements, Quality Notifications and Line-Side Pressure
AI for SAP S/4HANA in automotive connects a private language model to scheduling agreements, delivery schedules, QM inspection lots and quality notifications, so supply chain and quality teams can ask plain-English questions and get answers grounded in live data. It runs read-only on your own infrastructure, which matters when OEM drawings, volumes and pricing are under NDA.
Read the guideS/4HANA + chemicalsAI for SAP S/4HANA in Chemicals: Batch Characteristics, Process Orders and Regulated Substance Data
AI for SAP S/4HANA in chemicals reads batch classification, process orders, master recipes and EHS substance data through a private, read-only layer, so quality, planning and compliance staff can ask plain-English questions across them. It runs on your own infrastructure, which suits formulations and customer specifications that you treat as trade secrets.
Read the guideS/4HANA + pharma GxPAI for SAP S/4HANA in Pharmaceuticals: Read-Only, GxP-Aware Answers From Batch and Quality Data
AI for SAP S/4HANA in pharma works best as a read-only layer that answers questions about batches, QM inspection lots, deviations and stock without touching GxP records. It runs on your own infrastructure, shows the query behind each answer and keeps any write or disposition decision with qualified staff under your quality system.
Read the guideS/4HANA + machinery buildersAI for SAP S/4HANA in Industrial Machinery: Project Structures, Variant BOMs and Aftermarket Parts
AI for SAP S/4HANA in industrial machinery reads project structures, variant configurations, engineering changes and service data through a private, read-only layer, so project managers, engineers and service staff can ask plain-English questions across them. It runs on your own infrastructure, protecting machine designs and customer contracts.
Read the guideS/4HANA + food and beverageAI for SAP S/4HANA in Food and Beverage: Batch Traceability, Shelf Life and Recall Readiness
AI for SAP S/4HANA in food and beverage reads batch, shelf-life, quality and delivery records through a private, read-only layer so supply chain and quality staff can ask plain-English questions about traceability, aging stock and recall scope. It runs on your own infrastructure and shows the query behind each answer.
Read the guideS/4HANA MM procurement AIAI for SAP S/4HANA Procurement (MM) That Reads POs, Receipts and Invoices Together
AI for SAP S/4HANA procurement lets buyers and AP analysts ask questions across purchase requisitions, purchase orders, goods receipts and supplier invoices without knowing table names, and see the OData or CDS query behind the answer. It runs read-only against S/4HANA APIs, can sit on-prem or in a private cloud, and drafts follow-ups or PO changes for human approval.
Read the guideS/4HANA FI/CO close AIAI for SAP S/4HANA Finance Close (FI/CO) That Traces the Universal Journal for You
AI for SAP S/4HANA finance close lets controllers ask about postings, allocations, variances and open close tasks against the universal journal (ACDOCA) and get traceable answers with the CDS or OData source shown. It reduces time spent hunting for reconciling items, runs read-only, and drafts accrual or reclass proposals for accountants to approve, on-prem if required.
Read the guideS/4HANA SD order AIAI for SAP S/4HANA Sales and Distribution (SD) That Explains Orders, Pricing and Blocks
AI for SAP S/4HANA SD lets order management and customer service ask where an order is, why a delivery or billing is blocked, and how a price was determined, across sales documents, deliveries and billing documents. It reads through released APIs and CDS views, shows its queries, and drafts customer updates or block releases for human approval, on-prem or in a private cloud.
Read the guideSAP Business One purchasing AIAI for SAP Business One Purchasing, From Purchase Request to A/P Invoice
AI for SAP Business One purchasing lets buyers ask plain-English questions about purchase requests, purchase orders, goods receipt POs and A/P invoices and get answers built from live Business One data, with the query shown. It reads through the Service Layer or read-only SQL views, respects Business One authorizations, and can run on-prem so supplier prices and terms stay inside your network.
Read the guideSAP Business One inventory AIAI for SAP Business One Inventory: Stock, Batches, Bins and Valuation
AI for SAP Business One inventory answers questions about on-hand, committed and ordered quantities, batch and serial status, bin contents and valuation by querying live Business One data, with the query shown. It reads through the Service Layer or read-only SQL views and can run on-prem, so stock positions and costs never reach a public AI service.
Read the guideAI for Oracle
34 guidesE-Business Suite, JD Edwards, NetSuite, Fusion Cloud ERP, and Agile PLM.
AI for Oracle E-Business Suite, Without Leaving On-Prem
Oracle EBS 12.2 runs on-prem for good reasons, and Oracle's own commitment to premier support into 2036 means there is no forced clock to replatform before adding AI. A private LLM grounded on your EBS schema, concurrent program history, and interface tables can answer questions and triage exceptions today, entirely inside your network, with no data sent to a public model API.
Read the guideJD Edwards + AI agentsAI for JD Edwards EnterpriseOne, Built on Orchestrator and AIS
JD Edwards EnterpriseOne 9.2 already ships the integration surface an AI layer needs: Orchestrator for composed business processes, the AIS Server for REST access, and well-documented tables like F4211 and F0411. A private LLM grounded on that surface can answer sales order, purchase order, and account status questions without touching UBEs or custom RPG interfaces.
Read the guideNetSuite + grounded AIAI for NetSuite, Beyond the Built-In Text Tools
NetSuite's native AI features are mostly text generation, field defaulting, and forecasting assists inside the SuiteCloud platform, not grounded question-answering over your specific chart of accounts, saved searches, and transaction history. A private LLM built on SuiteQL and RESTlets closes that gap for CFOs who want direct answers without exporting financial data to a third party.
Read the guideFusion Cloud + private AIFusion Cloud ERP AI: OCI Generative AI Service vs. a Private LLM
Oracle's OCI Generative AI Service is a legitimate, Oracle-native way to add AI to Fusion Cloud ERP, and for a lot of use cases it is the simpler path. A private LLM grounded on Fusion data via REST APIs and OTBI becomes the better fit specifically when data sensitivity, model choice, or air-gapped requirements rule out Oracle's own AI service.
Read the guideEBS + ITAR-aware AIAI for Oracle EBS in Aerospace and Defense Manufacturing
Aerospace and defense manufacturers running Oracle EBS's Project Manufacturing and Project Costing modules need AI that respects export control segregation, not a generic ERP copilot. A private, on-prem model grounded on project, serial, and configuration data can answer operational questions without creating a new export control exposure.
Read the guideJD Edwards World + IBM iAI for JD Edwards World on IBM i, without a EnterpriseOne migration
JD Edwards World running on IBM i can get natural-language question answering and document AI today by reading DB2 for i physical files through a read replica or CDC feed, without touching green-screen programs or committing to a EnterpriseOne migration. The AI layer sits beside the box, on a Linux or Windows host with a GPU, and never writes back to World tables directly.
Read the guideNetSuite Manufacturing + AI agentsAI agents for NetSuite manufacturing operations
NetSuite's built-in manufacturing module covers work orders, routings, and WIP, but most operations managers still chase status by exporting saved searches or asking a planner. AI agents grounded in SuiteQL can answer work-order and routing questions directly and flag exceptions, while any write-back to NetSuite (rescheduling an operation, closing a work order) stays behind a human approval step.
Read the guideAgile PLM + ERP AIAI across Oracle Agile PLM and your ERP: engineering changes without the swivel chair
Agile PLM owns the engineering bill of materials and the change process; the ERP owns the manufacturing BOM, open orders, and inventory. An AI layer grounded on both systems can answer "what does this ECO actually affect" in one query, instead of an engineer manually cross-referencing Agile change orders against ERP work orders and open sales lines.
Read the guideOracle AI Buyer GuideOracle ERP AI Consulting: What a Partner Should Deliver
Oracle's ERP portfolio spans four architecturally distinct products: E-Business Suite on concurrent programs and interface tables, JD Edwards on Orchestrator and AIS, NetSuite on SuiteQL and RESTlets, and Fusion Cloud ERP on OCI. An AI consulting partner needs to name which one they actually know, not gesture at 'Oracle integration' generically, and be explicit about OCI GenAI versus a self-hosted private layer for sensitive data.
Read the guideOracle Cloud SCM + private AIAI for Oracle Cloud SCM and Manufacturing, Grounded in Your Planning Data
Oracle Cloud SCM and Fusion Manufacturing hold the demand plans, work orders, and supplier records a planner needs, but getting a straight answer out of them still means an OTBI report or a call to IT. A private AI layer reads those objects through Oracle's REST APIs and OIC, then answers planning questions, drafts supplier follow-ups, and explains exceptions in plain language, without supplier pricing or cost data leaving your boundary.
Read the guideJD Edwards Manufacturing + on-prem AIAI for JD Edwards EnterpriseOne Manufacturing, Without Leaving the F-Tables Exposed
JD Edwards EnterpriseOne Manufacturing runs shop floor operations through work orders, routings, and engineering change orders that most operations teams still chase through One View Reporting or a UBE someone half remembers. A private AI layer reads that data through Orchestrator Studio and the AIS server, answers questions in plain language, and never sends F-table data to a public model.
Read the guideNetSuite OneWorld + private AIAI for NetSuite OneWorld Multi-Subsidiary Consolidation
NetSuite OneWorld holds every subsidiary's ledger, but answering a consolidation question, such as which intercompany balance failed to eliminate this period, still means a controller running a saved search across subsidiaries by hand. A private AI layer queries OneWorld through SuiteQL and SuiteAnalytics, answers multi-subsidiary questions with the underlying query shown, and keeps financial data inside your own infrastructure.
Read the guidePrimavera P6 + ERP + private AIAI for Oracle Primavera P6 Linked to Your ERP's Actuals
Program teams running Oracle Primavera P6 for schedule and an ERP like Costpoint, SAP, or Oracle for cost and procurement actuals spend real hours each week reconciling the two by hand to answer a simple question: is this program on schedule and on cost. A private AI layer reads both systems, answers status and variance questions with activity IDs and cost account codes cited, and keeps contract and program data inside your own boundary.
Read the guideNetSuite + food safety AINetSuite AI for Food and Beverage: Lot Genealogy, Shelf Life and Recall Readiness
AI for NetSuite in food and beverage connects a private model to lot-numbered inventory, work orders, and receipts through SuiteQL and SuiteTalk, so quality and operations teams can ask which finished lots contain a given ingredient lot and get an answer with the query shown. It supports FSMA 204 traceability and mock-recall drills without moving recipes, supplier terms, or customer data to a public AI service.
Read the guideNetSuite + MedTech AINetSuite AI for Medical Device Manufacturers: Traceability, DHR Context and QMSR Readiness
AI for NetSuite in medical devices gives quality, operations, and regulatory teams a private, read-only way to ask questions across lot and serial numbers, work orders, shipments, and UDI-related item data, with the query shown. It is not a QMS and does not validate or replace one: it helps people find and cross-check records faster while the customer keeps ownership of its quality system and 21 CFR Part 11 obligations.
Read the guideNetSuite + CPG AINetSuite AI for Consumer Products: Retailer Compliance, Deductions and SKU Complexity
AI for NetSuite in consumer products lets operations, finance, and customer service teams ask plain-English questions about orders, fulfillments, retailer deductions, returns, and inventory across warehouses and 3PLs, with the query shown. It helps small teams manage large SKU counts and retailer requirements without exporting to spreadsheets or sending commercial terms to a public AI service.
Read the guideNetSuite + apparel AINetSuite AI for Apparel Manufacturing: Matrix Items, Seasons, Contractors and Traceability
AI for NetSuite in apparel lets merchandising, sourcing, and finance teams ask questions across matrix items, purchase orders, contract manufacturing work orders, landed cost, and wholesale and direct orders, with the query shown. It also helps trace materials back to suppliers for customs and forced-labor due diligence, while keeping line plans, costs, and factory terms on infrastructure the company controls.
Read the guideNetSuite + beauty AINetSuite AI for Cosmetics and Personal Care: MoCRA Records, Formula Changes and Batch Trace
AI for NetSuite in cosmetics and personal care helps regulatory, quality, and operations teams ask which products contain a given ingredient, which batches used a specific raw material lot, and what records exist for MoCRA-style recordkeeping, with queries shown. It runs privately so formulas, supplier pricing, and launch calendars stay inside the company, and it supports people rather than making compliance decisions.
Read the guideJDE process manufacturing AIAI for JD Edwards Process Manufacturing: Formulas, Lots, and Yields in Plain English
AI for JD Edwards process manufacturing connects a private language model to EnterpriseOne data (item, lot, formula, work order and cost tables) so planners, quality leads and plant managers can ask questions about yield variance, lot genealogy and formula cost and get answers grounded in live records. It runs on your infrastructure, is read-only by default, and respects JDE security.
Read the guideJDE food and beverage AIAI for JD Edwards Food and Beverage: Faster Traceability, Shelf Life, and Recall Drills
AI for JD Edwards in food and beverage connects a private model to lot, expiry, formula and sales data so teams can run traceability queries, shelf-life watchlists and allergen checks in plain English. Answers come from live EnterpriseOne records with the query shown, run on your own infrastructure, and support (not replace) your FSMA, HACCP and customer audit obligations.
Read the guideJDE chemicals AIAI for JD Edwards in Chemicals: Batch Formulas, Hazard Data, and Customer Specs
AI for JD Edwards in chemicals lets plant, quality and regulatory teams ask about batch formulas, lot potency, hazard classifications and customer specifications in plain English, with answers grounded in EnterpriseOne data and controlled documents. It runs privately, is read-only by default, and helps your teams find evidence faster for REACH, TSCA, GHS and OSHA obligations that remain yours.
Read the guideOracle EBS pharma AIAI for Oracle EBS in Pharmaceuticals: Batch Genealogy, Quality Data, and Validation-Aware Design
AI for Oracle E-Business Suite in pharmaceuticals gives quality, manufacturing and supply teams a private, read-only way to question batch, lot, quality-result and inventory data, with every answer tied to a visible query. It is designed to sit outside validated GxP decision paths until validated, supports data integrity principles, and does not replace EBS controls, e-signatures or QA judgement.
Read the guideOracle EBS industrial AIAI for Oracle EBS in Industrial Manufacturing: WIP, MRP, and BOM Questions Answered Directly
AI for Oracle E-Business Suite in industrial manufacturing lets planners, buyers and plant managers ask about WIP jobs, bills of material, MRP recommendations, inventory and purchasing in plain English, with answers grounded in live EBS data and the query shown. It runs privately on your infrastructure and reads rather than writes, so it adds visibility without touching your customisations.
Read the guideNetSuite + procurement AINetSuite Procurement With AI for Purchase Orders, Receipts and Vendor Bills
AI for NetSuite procurement reads purchase orders, item receipts, vendor bills and vendor records through SuiteQL and REST web services so buyers and AP can ask what is late, what does not match and which vendors drive exceptions. Proposed follow-ups and corrections are drafts for approval, and NetSuite roles and subsidiary restrictions still apply.
Read the guideNetSuite + inventory AINetSuite Inventory and Demand Planning With AI That Checks Your Reorder Logic
AI for NetSuite inventory and demand planning reads item records, location quantities, demand plans and order history to find stockout risks, excess stock and reorder points that no longer match demand. It supports planners with evidence and draft changes; NetSuite's own planning engine and approvals still decide and execute.
Read the guideNetSuite + close AINetSuite Month-End Close With AI That Finds the Problems Before the Deadline
AI for NetSuite month-end close queries the general ledger, subledgers, intercompany transactions and period close checklist to find unreconciled balances, unusual journal entries, missing accruals and open items blocking the period. It prepares evidence and draft entries for the controller; posting, approvals and period locking remain with authorised finance staff.
Read the guideNetSuite ARM + SuiteBilling AIAI for NetSuite Revenue Recognition and Billing That Explains Every Revenue Plan
AI for NetSuite revenue recognition and billing lets revenue accountants ask plain-English questions about revenue arrangements, revenue elements, revenue plans and billing schedules, and get answers grounded in live NetSuite data with the underlying SuiteQL or saved search shown. It is most useful for tracing why a revenue plan changed and finding ASC 606 or IFRS 15 exceptions before close, and it stays read-only unless a human approves a draft.
Read the guideNetSuite order to cash AIAI for NetSuite Order to Cash: Find Where Every Order and Dollar Is Stuck
AI for NetSuite order to cash answers questions across the sales order, item fulfillment, invoice and customer payment chain, such as which approved orders have not shipped, which shipments are not invoiced and which invoices are overdue and why. It reads live NetSuite data through SuiteQL, shows its queries, and can draft customer follow-ups for a person to approve.
Read the guideOracle EBS P2P + private AIAI for Oracle E-Business Suite Procure to Pay, From Requisition to Payment
AI for Oracle EBS procure to pay lets buyers, receiving staff and AP analysts ask plain-English questions across requisitions, purchase orders, receipts and Payables invoices, answered from live EBS data with the SQL shown. It reads through read-only database access or Integrated SOA Gateway services under EBS security, and can run fully on-prem.
Read the guideOracle EBS order management AIAI for Oracle EBS Order Management, From Booking to Invoice
AI for Oracle EBS Order Management answers plain-English questions about booked orders, holds, reservations, pick release, shipping and AutoInvoice status from live EBS data, with the SQL shown. It reads through a read-only database account or Integrated SOA Gateway, respects operating unit security, and can run on-prem so customer orders and pricing stay in your network.
Read the guideJD Edwards procurement AIAI for JD Edwards EnterpriseOne Procurement, From Requisition to Voucher
AI for JD Edwards EnterpriseOne procurement answers questions about purchase orders, receipts and vouchers by reading live data from tables such as F4301, F4311, F43121 and F0411, with the query shown. It connects through read-only database access or AIS and Orchestrator services under E1 security, and can run on-prem so supplier pricing stays in your network.
Read the guideJDE inventory + manufacturingAI for JD Edwards Inventory Management and Shop Floor Manufacturing
AI for JD Edwards inventory and manufacturing reads Item Ledger (F4111), Item Location (F41021), work order (F4801) and MRP data through AIS and read-only database access, then answers planner and buyer questions in plain English. It explains shortages, cost variances and MRP messages with the underlying rows shown, and any change is proposed for a person to approve.
Read the guideFusion procurement + AIAI for Oracle Fusion Cloud Procurement: Requisitions, POs and Suppliers
AI for Oracle Fusion Procurement reads requisitions, purchase orders, receipts and supplier records through Fusion REST APIs and BICC or OTBI extracts, then answers buyer and category manager questions in plain English. It explains late supply, price variance and match exceptions, and drafts actions for approval inside Fusion rather than writing around its workflow.
Read the guideFusion close + AIAI for the Oracle Fusion Financial Close: Journals, Subledgers and Flux
AI for the Oracle Fusion financial close reads General Ledger, subledger and intercompany data through REST and BICC extracts, then tracks close status, explains journal exceptions and drafts flux commentary. Controllers get sourced answers faster, while period status, journals and approvals stay inside Fusion with their audit trail.
Read the guideAI for Microsoft Dynamics
17 guidesDynamics 365 Finance & Supply Chain, Business Central, AX 2012, GP, and NAV.
AI for Dynamics 365 Finance and Supply Chain beyond Copilot
Microsoft's own Copilot features in D365 Finance and Supply Chain Management are real but narrow, mostly summarization and drafting inside specific workspaces, and run through Microsoft's cloud AI services by default. A private LLM grounded on D365's data entities and OData/custom services can answer broader operational questions, run fully on-prem for the Local Business Data deployment option, and keep sensitive supply chain and financial data off a shared AI service where that matters.
Read the guideBusiness Central + private AIAI for Dynamics 365 Business Central, on-premises or SaaS, beyond Copilot
Business Central's built-in Copilot features cover specific tasks well, like drafting item descriptions or bank reconciliation matching, but leave most finance and operations questions unanswered without a report. A private AI layer grounded on BC's OData v4 web services and APIs can answer broader questions directly, works the same way whether BC is on-premises or SaaS, and keeps financial data off a shared cloud AI service where that matters.
Read the guideAX 2012 + private AIAI for Dynamics AX 2012, Without the Forced Migration
Dynamics AX 2012 R3 is past mainstream Microsoft support, but the AOT, the SQL Server database, and years of customizations still run the business. A private LLM can sit beside AX today, answering questions and drafting documents from AX data on-prem, and later double as the documentation and mapping engine for a Dynamics 365 or other ERP migration.
Read the guideGP / NAV + private AIAI for Dynamics GP and NAV, Before the Business Central Decision
Microsoft has set an end date for Dynamics GP mainstream support, and Dynamics NAV has not received new feature investment for years, with most active development going to Business Central. A private LLM grounded in the GP or NAV SQL Server database gives finance and operations staff faster answers today, independent of when or whether a Business Central migration happens.
Read the guideDynamics SL + on-prem AIAI for Dynamics SL, grounded on your project and contract data
Dynamics SL still runs project accounting and DCAA-compliant timesheets for a lot of government contractors who have no plan to move off it soon. A private LLM grounded read-only on SL's SQL Server database can answer project, contract, and indirect rate questions in plain language, and can run entirely on infrastructure a CIO controls, without sending CUI or contract cost data to a public AI API.
Read the guideDynamics NAV + on-prem AIAI for Dynamics NAV 2009-2018, before or instead of a Business Central migration
Dynamics NAV 2009 through 2018 still runs manufacturing and distribution operations at a large number of companies with no immediate plan to move to Business Central. A private LLM grounded on NAV's SQL Server database through its own naming conventions can answer inventory, order, and production questions directly, without touching a single C/AL object or requiring the migration first.
Read the guideBusiness Central Manufacturing + AIAI for Business Central manufacturing operations, beyond Copilot
Business Central's Copilot features are real, but they lean toward sales text and marketing content, not shop-floor questions. An AI layer grounded on Business Central's manufacturing data through its OData and REST APIs can answer production order, routing, and capacity questions directly, with any write-back handled through an approved AL extension rather than a blind automation.
Read the guideD365 SCM + on-prem AIAI for Dynamics 365 Supply Chain Management on Local Business Data
Dynamics 365 Supply Chain Management's Local Business Data (LBD) option runs transactional workloads on Azure Local infrastructure a manufacturer controls, built for plants with limited or intermittent connectivity. A private LLM grounded on that same edge environment can answer production and warehouse questions without depending on cloud reachability, but LBD is closer to a disconnected-edge model than a fully air-gapped one, and an IT director should understand that distinction before assuming it satisfies an air-gap requirement.
Read the guideBusiness Central + discrete mfg AIAI for Dynamics 365 Business Central in Discrete Manufacturing
AI for Business Central in discrete manufacturing connects a private language model to production orders, production BOMs, routings, item ledger entries and the planning worksheet, so planners and plant managers can ask plain-English questions and get answers grounded in live data with the query shown. It runs next to your tenant or on-prem server, reads through API pages and OData, and drafts changes for human approval instead of writing silently.
Read the guideBusiness Central + food AIAI for Dynamics 365 Business Central in Food and Beverage Manufacturing
AI for Business Central in food manufacturing reads lot numbers, expiration dates, production BOMs and item ledger entries so quality and operations teams can ask for forward and backward traces, shelf-life risk and yield variance in plain English. A private model grounds every answer in your records and shows the query, which helps with recall drills and FSMA record requests.
Read the guideBusiness Central + fabrication AIAI for Dynamics 365 Business Central in Metal Fabrication
AI for Business Central in metal fabrication reads items, variants, production orders, routings, subcontracting and lot records so estimators, schedulers and quality staff can ask about job cost, heat-lot traceability and outside-processing status in plain English. A private model grounds answers in your data and shows the query, while quoting and write-backs stay as drafts for approval.
Read the guideBusiness Central + medtech AIAI for Dynamics 365 Business Central in Medical Device Manufacturing
AI for Business Central in medical device manufacturing helps quality and operations teams retrieve lot and serial genealogy, assemble device history record evidence and prepare audit packages from ERP data and controlled documents, using a private model with every answer tied to its source records. It supports QMSR and ISO 13485 workflows but does not replace a validated QMS or regulatory judgment.
Read the guideBusiness Central + equipment AIAI for Dynamics 365 Business Central in Industrial Equipment Manufacturing
AI for Business Central in industrial equipment connects a private model to projects, assembly orders, production BOMs, service orders and warranty data, so general managers, project leads and service teams can ask about build status, cost to complete, installed-base history and spare parts in plain English. Answers cite the underlying records and any change stays a draft for approval.
Read the guideD365 F&SCM procurementAI for Dynamics 365 Finance and Supply Chain Procurement
AI for Dynamics 365 F&SCM procurement reads purchase orders, product receipts, vendor invoices and vendor records through OData data entities or Synapse Link exports, then answers buyer and AP questions in plain English. It explains late deliveries, price variance and three-way match failures, with changes drafted for approval in the application.
Read the guideD365 production controlAI for Dynamics 365 Production Control: Orders, Planning and Scheduling
AI for Dynamics 365 production control reads production orders, BOM and route lines, planned orders and shop floor reporting through data entities and exports, then explains late orders, shortages and plan changes in plain English. It supports planners and supervisors, while scheduling, release and reporting stay in D365 with their normal controls.
Read the guideBusiness Central purchasing AIAI for Business Central Purchasing: Questions, Exceptions and Draft Orders on Your Own Data
AI for Business Central purchasing connects a language model to the Purchase Header, Purchase Line, Vendor and posted receipt data through the standard API v2.0 and OData web services, so buyers can ask which orders are late, which vendors are drifting, and what is waiting for invoice match. Answers cite the records behind them, access is read-only by default, and any draft order or change goes through a human before it posts.
Read the guideBusiness Central inventory AIAI for Business Central Inventory: Ask About Stock, Bins, Costing and Variances
AI for Business Central inventory lets planners and warehouse leads ask questions about stock, bin content, reorder policy and cost in plain English, answered from the Item Ledger Entry, Warehouse Entry and Value Entry data with the query shown. It finds mismatches between inventory and general ledger, ranks slow stock, and proposes count and reorder-point changes for human approval.
Read the guideAI for Epicor
16 guidesKinetic, Prophet 21, and legacy Vantage/E9.
AI for Epicor Kinetic, Beyond What Prism Covers
Epicor Kinetic already exposes a rich data model through BAQs (Business Activity Queries), BPM (Business Process Management) directives, and the REST v2 API, and Epicor's own Prism AI features cover a growing set of embedded scenarios. A private LLM grounded in that same BAQ and REST layer extends coverage to ERP manager-specific questions, cross-module agents, and on-prem deployment where Prism's cloud dependency is not an option.
Read the guideProphet 21 + private AIAI for Epicor Prophet 21, Built for Distribution Operations
Prophet 21 (P21) runs order entry, inventory, purchasing, and pricing for a large base of industrial and wholesale distributors, and most of the daily operational questions (stock availability, order status, replenishment timing) are exactly what a retrieval layer over P21's SQL Server database answers well. On-prem or private-cloud deployment keeps customer and pricing data inside the distributor's own network.
Read the guideVantage / Vista / E9 + private AIAI for Epicor Vantage, Vista, and E9, and a Real Kinetic Upgrade Path
Epicor Vantage, Vista, and E9 are prior-generation products built on Progress OpenEdge, still running production for manufacturers who have not yet moved to Kinetic. A private LLM can query that OpenEdge database directly today, and the customization and usage inventory it builds becomes the evidence base for a properly scoped Kinetic upgrade.
Read the guideEpicor Eclipse + private AIAI for Epicor Eclipse, Without Sending Distributor Data Off-Site
Adding AI to Epicor Eclipse means grounding a private, on-prem model on your Eclipse SQL Server database (order entry, item master, rebates, warehouse) so counter staff, sales, and finance can ask questions in plain English and get answers with the underlying query shown. It works alongside your existing Eclipse reporting and EDI setup and runs on hardware you control, so pricing, rebate agreements, and customer data never leave your network.
Read the guideKinetic Cloud + private AIPrivate AI for Epicor Kinetic Cloud, Outside the Multi-Tenant Boundary
Epicor Kinetic Cloud runs as multi-tenant SaaS, which is fine for most workloads but raises a real question for manufacturers with export-controlled BOMs, routings, or customer IP: where does an AI feature actually process that data. A private AI layer grounds a self-hosted model on Kinetic's REST v2 API, BAQs, and Data Fabric, running on infrastructure you control, so the answer to that question is simply nowhere near Epicor's multi-tenant environment.
Read the guideEpicor CMS legacy manufacturingAI for Epicor CMS, the Automotive ERP Running on IBM i
Epicor CMS runs the sequencing, kanban, and EDI backbone for many automotive suppliers on IBM i (AS/400), a system with no modern REST API and a shrinking pool of staff who know it well. A private AI layer reads from a replicated DB2 database and EDI transaction logs to answer questions in plain English, root-cause ASN and chargeback disputes, and capture retiring staff's tribal knowledge before it walks out the door.
Read the guideTropos + private AIAI for Epicor Tropos, Built for How Configure-to-Order Manufacturing Actually Works
Epicor Tropos runs configure-to-order and mill-based manufacturing for building products and forest products companies, where attribute-driven BOMs and multiple unit-of-measure conversions (board feet, lineal feet, each) make ad hoc questions genuinely hard to answer quickly. A private AI layer grounds a model on your Tropos database so CSRs, schedulers, and estimators can ask questions in plain English, with answers sourced from the same configuration and cost data Tropos already tracks.
Read the guideKinetic + fabrication shopsEpicor Kinetic AI for Metal Fabrication: Jobs, Heat Numbers and Outside Processing in Plain English
AI for Epicor Kinetic in a metal fabrication shop lets estimators, planners and quality leads ask questions about jobs, materials, heat numbers, outside processing and quotes in plain English and get answers grounded in live Kinetic data, with the query shown. It runs on-prem or in a private cloud, is read-only by default, and respects Kinetic security so customer drawings, pricing and material certs stay inside your boundary.
Read the guideKinetic + injection moldingEpicor Kinetic AI for Injection Molders: Resin, Tooling, Scrap and Cycle Time Questions Answered
AI for Epicor Kinetic in an injection molding plant lets planners, process engineers and quality staff ask about resin lots, mold utilisation, scrap, regrind and cycle-time variance in plain English and get answers grounded in live Kinetic records. It runs on-prem or in a private cloud, is read-only by default, and keeps customer part data and pricing inside your boundary.
Read the guideKinetic + machinery OEMsEpicor Kinetic AI for Industrial Machinery Builders: BOMs, ECOs, Project Jobs and Spares
AI for Epicor Kinetic in an industrial machinery company lets project managers, engineers, planners and service teams ask about multi-level BOMs, engineering changes, project job status, long-lead purchases and spare parts in plain English and get answers from live Kinetic data. It runs privately on-prem or in your cloud tenant, is read-only by default, and cites the records behind each answer.
Read the guideKinetic + medical devicesEpicor Kinetic AI for Medical Device Manufacturers: Traceability, CAPA and DHR Questions, Read-Only
AI for Epicor Kinetic in a medical device company gives quality, regulatory and operations staff a read-only way to ask about lot and serial traceability, nonconformances, CAPA status, supplier history and device history record contents in plain English, with sources cited. It runs on-prem or in a private cloud, sits outside the validated quality record, and leaves compliance decisions with your quality system.
Read the guideKinetic + automotive suppliersEpicor Kinetic AI for Automotive Suppliers: EDI Releases, ASN, PPAP and Scorecard Questions
AI for Epicor Kinetic in an automotive supplier lets planners, customer service, quality and plant managers ask about EDI releases, shipments, ASN status, PPAP documents, scrap and customer scorecards in plain English, with answers grounded in live Kinetic data and queries shown. It runs on-prem or in a private cloud, is read-only by default, and keeps OEM program data inside your boundary.
Read the guideEpicor Kinetic + job shopsAI for Epicor Kinetic Job Shops: Quote History, Job Variance and Schedule Risk
AI for an Epicor Kinetic job shop connects a private model to Kinetic's REST API and Business Activity Queries so estimators, schedulers and managers can ask about quote history, job variance and late subcontract operations in plain English. It respects Kinetic security, is read-only by default, and shows the query used for each answer.
Read the guideEpicor Kinetic quoting AIAI for Epicor Kinetic Estimating and Quoting: Faster Quotes Grounded in Your Own Job History
AI for Epicor Kinetic estimating reads an RFQ, finds similar past parts and jobs, and prepares a draft quote with materials, operations and quantity breaks for an estimator to check. It compares planned cost with job actuals so estimates improve over time, uses the Kinetic REST API and BAQs, and can run on-prem so customer drawings and pricing stay private.
Read the guideEpicor Kinetic scheduling AIAI for Epicor Kinetic Scheduling: Explain Late Jobs, Find Conflicts, Tune the Plan
AI for Epicor Kinetic scheduling reads jobs, operations, resources and shop-floor actuals through the Kinetic APIs and BAQs to explain why a job is late, find overloaded resources and compare scheduled with actual run times. It proposes changes for a planner to apply, and it does not replace Kinetic's scheduling engine or an advanced planning tool where one is licensed.
Read the guideEpicor Kinetic quality AIAI for Epicor Kinetic Quality: Non-Conformances, CAPA, Inspection and Traceability
AI for Epicor Kinetic quality reads non-conformance, inspection, lot and supplier data to summarize defects, find repeat causes and prepare CAPA drafts for a quality engineer to review. It traces lots from supplier to customer, answers audit questions with record links, and can run on-prem so quality records stay inside your regulated environment.
Read the guideAI for IFS
4 guidesIFS Cloud and Maintenix for aerospace, defense, and MRO.
AI for IFS Cloud in aerospace and defense manufacturing
IFS Cloud programmes in aerospace and defense sit on IFS's projection layer (OData-based) and Aurena UI, with heavy use of project-based manufacturing, configuration management, and document control. A private LLM grounded on those projections, run on infrastructure the programme controls, gives engineers and planners natural-language answers without exporting export-controlled or classified-adjacent data to a multi-tenant AI service.
Read the guideMaintenix + on-prem AIAI for IFS Maintenix in aviation MRO
IFS Maintenix holds the aircraft maintenance history, task cards, and airworthiness directive (AD) and service bulletin (SB) compliance record that keeps a fleet legally flying. A private AI layer grounded on Maintenix data can answer technical records questions, help planners triage AD/SB applicability, and speed up document assembly for a check, without sending maintenance records to an external service.
Read the guideIFS Cloud + maintenance AIAI for IFS Cloud Maintenance: Work Orders, PM Plans and Spares Answered in Plain English
AI for IFS Cloud maintenance lets planners, reliability engineers and technicians ask plain-English questions across work orders, equipment objects, preventive maintenance plans, spare parts and fault history. It reads IFS data through its APIs with the asker's own permissions, shows the query behind each answer, and can run on your own infrastructure.
Read the guideIFS Cloud + project AIAI for IFS Cloud Project Management: Cost, Schedule and Forecast Questions Answered
AI for IFS Cloud project management lets project controllers and managers ask plain-English questions about budget versus actual, activities, milestones, forecasts and project-linked purchasing and inventory. The assistant reads IFS data with the user's permissions, shows its queries, and can run on your own servers.
Read the guideAI for Deltek
4 guidesCostpoint for government and defense contractors under DCAA and CMMC.
AI for Deltek Costpoint in government contract accounting
Deltek Costpoint runs the accounting backbone for most US government contractors: timesheets, indirect rate pools, incurred cost submissions, and project-based revenue recognition. A private AI layer grounded on Costpoint's project, labor, and GL data can draft variance commentary, triage timesheet exceptions, and support incurred cost preparation, while keeping the underlying data inside infrastructure the CFO's office controls.
Read the guideCostpoint + CMMC-scoped AIOn-prem AI for Costpoint inside a CMMC Level 2 enclave
A contractor running Deltek Costpoint inside a CMMC Level 2 scoped enclave, often on GCC High or an equivalent environment, has to treat any new AI tool as a potential new CUI data flow. Running the model and retrieval layer inside the same enclave, rather than calling an external AI API, is the deployment pattern that avoids adding an unassessed boundary to your SSP.
Read the guideCostpoint + timekeeping AIAI for Deltek Costpoint Timekeeping and Labor: Timesheet Exceptions and Labor Questions
AI for Costpoint timekeeping reads timesheet, charge code and labor distribution data to flag late, missing, unusual or corrected entries and to answer labor cost questions in plain English. It is read-only by default, runs inside your network, and supports your compliance staff without replacing Costpoint controls or approvals.
Read the guideCostpoint + billing AIAI for Deltek Costpoint Project Accounting and Billing: Funding, Rates and Invoice Questions
AI for Costpoint project accounting and billing answers plain-English questions about project cost, funding, ceilings, indirect rates, revenue and invoice readiness by reading Costpoint data with the asker's permissions. It runs inside your network, shows its queries, and leaves billing, posting and rate changes to controlled Costpoint processes.
Read the guideAI for Specialist ERPs
73 guidesQAD, Plex, Sage X3, Acumatica, SYSPRO, Global Shop, ECI JobBOSS, ProShop.
AI for QAD Adaptive ERP in automotive and industrial manufacturing
QAD Adaptive ERP, and the Enterprise Edition installations still common in automotive and industrial supply chains, hold detailed EDI, scheduling, and quality data that most CIOs would rather ground a private AI model on than route through a general-purpose cloud AI tool. A private LLM connected through QAD's API layer or QXtend can answer scheduling and supplier questions, draft EDI exception follow-ups, and support quality documentation, without exporting sensitive supply chain data.
Read the guidePlex + private AIAI for Plex Manufacturing Cloud, Grounded in Your Production Data
AI on Plex Manufacturing Cloud works by replicating your production, quality, and genealogy data out of Plex through the Plex Data Source (PDS) and Plex Web Services into a data layer you control, then running a private LLM against that layer for question answering, exception triage, and copilots. Because Plex itself is single-tenant SaaS with no on-prem edition, full air-gapping is not possible, but the AI and the replicated data mart can run in your own VPC or on-site GPU box, and every write suggestion still goes back through the Plex API with a human approving it. Plant managers get a tool that reads Andon, SPC, and container genealogy data in plain language instead of another dashboard to interpret.
Read the guideSage X3 + private AIAI for Sage X3, Grounded in Your Own Ledgers and Stock Data
AI on Sage X3 works by connecting to the platform's own web services and REST endpoints, built on the Syracuse/Adonix X3 4GL framework, to ground a private language model on your general ledger, stock, and order data without that data ever reaching a public model provider. For a finance director running Sage X3 in the UK or France, that means natural-language variance explanations, faster month-end close narratives, and AP matching assistance grounded in your own folders and dimensions, hosted on infrastructure you or your chosen cloud partner control. Sage X3 can be deployed on-premise, in a private cloud, or as Sage-hosted SaaS, and the AI layer follows whichever of those the finance and IT teams have chosen for the core system.
Read the guideAcumatica + private AIAI for Acumatica Manufacturing Edition, Built on Your Own Data
AI on Acumatica Manufacturing Edition works by connecting to Acumatica's own REST API and OData endpoints, and its Generic Inquiries, to ground a language model on your BOMs, routings, production orders, and job costs. Because Acumatica supports both its SaaS offering and self-hosted or private-cloud licensing, an owner running Manufacturing Edition can choose to keep the AI layer entirely on infrastructure they control, answering questions like 'which jobs are losing money and why' in plain language instead of building another Generic Inquiry every time a new question comes up.
Read the guideSYSPRO + private AIAI for SYSPRO, Grounded in Your Own Manufacturing Data
AI on SYSPRO works by connecting to the platform's e.Net Solutions web services and REST APIs to ground a private language model on your inventory, job costing, and MRP data, without that data leaving the infrastructure you control. For a CIO running SYSPRO in Australia, South Africa, or North America, that means a question-answering assistant and agents grounded in your own SQL Server database and e.Net endpoints, deployed on-premise or in a private cloud rather than depending on a public model API for day-to-day manufacturing questions.
Read the guideGlobal Shop Solutions + private AIAI for Global Shop Solutions, Built for How Job Shops Actually Run
AI on Global Shop Solutions (GSS) works by connecting to the platform's API and report data on top of its SQL Server database to ground a private language model on your quotes, scheduling, and job cost history, so a job shop owner can ask plain-language questions instead of pulling another Crystal Reports export. Because most GSS shops already run the system on their own server, the AI layer can usually sit on the same on-premise infrastructure, keeping quoting and customer data inside the shop rather than sending it to a public model API.
Read the guideJob shop ERP + AIAI for ECI JobBOSS2 and E2 Shop System
You add AI to JobBOSS2 or E2 Shop System by reading job cost history, routings, and the scheduling board straight out of the SQL Server database that already runs your shop, then grounding a private language model on that data so it can draft quotes, flag at-risk jobs, and answer floor questions. Nothing needs to move to a public cloud API, and the model runs on hardware you own or control. For a shop with a handful of estimators and a scheduling board that changes hourly, the value shows up first in quoting speed and second in fewer 'where is my job' interruptions.
Read the guideProShop + on-prem AIAI for ProShop ERP in AS9100 Machine Shops
You add AI to ProShop by reading the process documents, NCRs, CAPAs, and traveler records that ProShop already centralizes, then grounding a private model on that data so it can draft NCR descriptions, summarize CAPA history for a part number, and answer 'what does the work instruction say' questions on the floor. Because ProShop already unifies quality and production data in one paperless system, the connector work is simpler than most ERPs; the harder part is keeping AS9100 traceability intact so every AI-drafted answer still points back to a controlled document revision.
Read the guideOdoo Manufacturing + private AIAI for Odoo Manufacturing That Reads Your Own Database, Not a Vendor's Cloud
AI for Odoo connects a private LLM to the Odoo ORM and PostgreSQL database so planners, buyers, and shop-floor leads can ask plain-English questions about mrp.production, mrp.bom, and stock.quant and get answers grounded in live data, with the underlying domain filter shown. It runs on-prem or in a private cloud next to your existing Odoo Community or Enterprise instance, so customer BoMs, costs, and supplier terms never leave your infrastructure.
Read the guideERPNext + Frappe + private AIAI for ERPNext, Built on the Frappe Framework You Already Run
AI for ERPNext connects a private LLM directly to Frappe's REST API and DocType model, so manufacturing, purchasing, and finance staff can ask plain-English questions about Work Orders, BOMs, and Job Cards and get answers grounded in live Frappe data. It runs beside your self-hosted bench or Frappe Cloud instance, keeping business data inside infrastructure you control.
Read the guidexTuple + on-prem AIAI for xTuple ERP, Grounded Directly in the PostgreSQL It Already Runs On
AI for xTuple connects a private LLM directly to xTuple's PostgreSQL database, including the stored procedures and views that hold most of its business logic, so operations and finance staff can ask plain-English questions and get answers grounded in live manufacturing order and inventory data. Because xTuple already keeps its core logic in the database rather than a separate application tier, this is often a more direct integration than with ERPs that hide logic behind a proprietary API.
Read the guideDolibarr + private AIAI for Dolibarr ERP, Without Sending Small-Business Data to a Cloud AI Vendor
AI for Dolibarr connects a private LLM to Dolibarr's REST API and MySQL/MariaDB tables so an owner or small operations team can ask plain-English questions about stock, invoices, and manufacturing orders and get grounded answers, without paying for or trusting a third-party cloud AI service. It runs on the same self-hosted LAMP-style server most Dolibarr instances already use.
Read the guideSage 100 + private AIAI for Sage 100, Grounded in Your MAS90/200 Data
AI on Sage 100 works by reading the underlying MAS90/200 Btrieve or SQL data (via the Sage 100 ODBC driver, Business Object framework, or the Sage 100 API for SQL-based installs) and grounding a private LLM on that schema, so an operations or office manager can ask plain-language questions about jobs, work orders, and inventory instead of building a Crystal Report or a Visual Integrator import. Because most Sage 100 shops are small to mid-size manufacturers running the SQL edition on-site or in a hosted private environment, the AI layer can sit in the same network boundary and never touch a public API. The result is a copilot that speaks MAS90 terms, item codes, and work order numbers back to the person who already knows the business, without requiring them to learn the Business Framework or hire a VAR for every new report.
Read the guideSage 300 + private AIAI for Sage 300, Built for Multi-Entity Manufacturers
AI on Sage 300 works by reading the Sage 300 database directly (SQL Server or the older Pervasive/Btrieve engine, depending on install) through the Sage 300 SDK's Business Logic Objects or the newer Sage 300 Web API, then grounding a private LLM on that data so multi-entity, multi-currency manufacturers get plain-language answers across companies without exporting data to a public model. Sage 300 (formerly Accpac) is common among manufacturers running several legal entities or currencies on one platform, and the multi-company structure is exactly where a natural-language layer earns its keep, letting an IT director or controller ask cross-entity questions that would otherwise need a report built and run separately per company database. Every AI-suggested action routes back through the Sage 300 API, never a raw table write, so the platform's own validation and multi-currency logic stays intact.
Read the guideSage Intacct + private AIAI for Sage Intacct, Grounded in Your Dimensional GL
AI on Sage Intacct works by reading data through the Sage Intacct XML/REST API (the platform's core integration surface, since Intacct is multi-tenant SaaS with no on-prem database to query directly) and grounding a private LLM on the resulting extract, including Intacct's dimensional structure of departments, locations, classes, and custom dimensions, so a finance team gets plain-language answers that respect the same multi-entity, multi-book structure they already report on. Because Intacct itself cannot be air-gapped, the privacy control point shifts to where the replicated data and the model run: a customer-controlled VPC or on-prem GPU box rather than a shared multi-tenant AI service, with every AI-suggested journal entry or reclassification going back through the Intacct API for a human to approve.
Read the guideQuickBooks Enterprise + private AIAI for QuickBooks Enterprise, Built for Small Manufacturers
AI on QuickBooks Enterprise works by reading the company file through the QuickBooks SDK (qbXML/QBFC) or a third-party ODBC driver such as QODBC, then grounding a private LLM on that data so an owner running a small manufacturing or job shop business gets plain-language answers about inventory, jobs, and cash without hiring a bookkeeper to build a custom report. Because QuickBooks Desktop Enterprise is a local company file (not a cloud service), the AI layer can run entirely on-site or in a small private-cloud footprint, with no requirement to send financial data anywhere external. Every AI-suggested transaction still goes back through the QuickBooks SDK, respecting QuickBooks' own audit trail rather than writing to the file directly.
Read the guideDELMIAworks / IQMS + on-prem AIAI for DELMIAworks (IQMS), grounded in your production monitoring data
Adding AI to DELMIAworks (formerly IQMS, EnterpriseIQ) means grounding a model on the Production Monitoring, quality, and job data already sitting in your SQL Server database, then running it on your own hardware so plant data never leaves the building. It answers press-and-mold questions in plain language, drafts CAPA and 8D reports from quality records, and rolls up scrap and downtime across plants without a canned Crystal Report.
Read the guideECI M1 + private AIAI for ECI M1, grounded in your job, quoting, and purchasing data
ECI M1 runs make-to-order and mixed-mode manufacturers on job costing, MRP, and shop-floor scheduling, but most shops still answer 'what's my WIP right now' by exporting a report to Excel. A private AI layer grounded on M1's job, quoting, and purchasing data answers those questions directly, drafts quotes from similar past jobs, and flags purchasing exceptions before they hit a job's delivery date, without changing how M1 itself runs.
Read the guideECI Macola + private AIAI for ECI Macola, from Progression Series to Macola 10
Macola shops split between the legacy Progression Series on Progress OpenEdge and the newer SQL Server-based Macola 10 / Macola ES, and both leave day-to-day questions, backorders, on-hand, open POs, answered by whoever knows the Order Entry and Inventory Management screens or can run Visual Integrator. A private AI layer grounded on either database answers those questions directly, triages VI job failures, and helps with the data-quality work that any eventual migration off Progression Series will require.
Read the guideExact Online + private AIAI for Exact Online, private and grounded in your own data
Exact Online is true multi-tenant SaaS, so 'on-prem AI for Exact Online' really means a private model beside it, grounded on data pulled through Exact's REST API into a tenancy you control, not inside Exact's own cloud. Done that way, finance and operations teams get plain-language answers on cash position, AR aging, and multi-division consolidation without waiting on a Power BI report or hitting Exact's default reporting.
Read the guideFishbowl + private AIAI for Fishbowl Manufacturing and Warehouse
AI on Fishbowl means grounding a private model on the Fishbowl database (work orders, BOMs, inventory, sales orders) and the QuickBooks ledger it syncs to, so operations staff can ask plain-language questions and get agent help on reorder points, work order status, and stock discrepancies without opening another report builder or sending data to a public AI vendor.
Read the guideKatana + private AIAI for Katana Cloud Manufacturing
AI on Katana means grounding a model on the Katana API alongside the Shopify, WooCommerce, or QuickBooks Online connections a maker or small manufacturer already runs, so an owner or ops lead can ask one question and get an answer that spans inventory, orders, and cost without stitching together exports from three tools by hand.
Read the guideMRPeasy + private AIAI for MRPeasy Manufacturing ERP
AI on MRPeasy means grounding a private model on MRPeasy's own REST API and connected QuickBooks Online or Xero ledger, so planners and operations managers can ask plain questions about production, inventory, and lot traceability and get exception and follow-up agents, instead of exporting MRPeasy's canned reports into a spreadsheet to piece the answer together.
Read the guideCetec ERP + private AIAI for Cetec ERP Electronics Manufacturing
AI on Cetec ERP means grounding a private model on Cetec's browser-native, all-in-one data (quoting, MRP, shop floor, quality) so EMS and PCB shops can get natural-language answers on job genealogy, cost rollups, and NCR history, deployed in a way that keeps customer BOMs and IP-sensitive program data off any shared or public AI service.
Read the guideGenius ERP + private AIAI for Genius ERP, Grounded in Your Engineering and Job Cost Data
AI on Genius ERP works by connecting to its SQL Server database and reporting views, plus its API and OData-style integration layer, to ground a private language model on your BOMs, routings, production schedule, and job costs. For an owner running a custom or engineer-to-order shop on Genius ERP, that means plain-language answers about job margin, capacity, and quoting history, hosted on infrastructure you control rather than a public AI service. Genius ERP is typically deployed on customer-managed SQL Server or as Genius ERP Cloud, and the AI layer can follow either path.
Read the guideEncompix + private AIAI for Encompix, Grounded in Your Project and Job Cost Data
AI on Encompix, the ECI Software Solutions ERP built around project-based engineer-to-order manufacturing, works by connecting to its Microsoft SQL Server database and reporting layer to ground a private language model on your project structures, estimates, engineering change orders, and job cost transactions. For an operations manager running heavy equipment or capital equipment production on Encompix, that means plain-language answers about project margin and schedule risk, on infrastructure the manufacturer controls rather than a public AI service.
Read the guideOptiProERP + private AIAI for OptiProERP, Grounded in Your SAP Business One Data
AI on OptiProERP works by connecting to the SAP Business One Service Layer and DI API that OptiProERP itself is built on, to ground a private language model on your manufacturing orders, MRP results, and quality data without that data reaching a public model provider. For a CIO evaluating AI for a mid-market manufacturer or distributor on OptiProERP, that means natural-language production and inventory answers grounded in SAP B1's own tables and user-defined fields, hosted on infrastructure the company controls, whether SAP B1 runs on SQL Server or SAP HANA.
Read the guideRootstock + private AIAI for Rootstock, Grounded in Your Salesforce-Native ERP Data
AI on Rootstock, the manufacturing ERP built natively on the Salesforce Platform, works by connecting to Rootstock's own Salesforce objects through the Salesforce REST, SOAP, and Bulk APIs to ground a private language model on your sales orders, work orders, BOMs, and routings. Because Rootstock runs entirely inside Salesforce's multi-tenant cloud with no on-premise edition, full air-gapping is not possible, but the AI and its data extract can run in an IT director's own VPC or on-site GPU hardware, with every write suggestion still going back through the Salesforce API with a human approving it.
Read the guidePriority ERP + private AIAI for Priority ERP: grounded answers without leaving your data plane
Adding AI to Priority ERP means building a connector layer over the Priority API or a read-replica of the underlying SQL Server or Oracle database, then grounding a model on that data with role-aware access controls. Priority shops run lean IT teams and heavily tabgen-customized screens, so the real work is mapping those custom fields into a documented semantic layer before any model sees them.
Read the guideabas ERP + private AIAI for abas ERP: grounded answers over your OpenAccess data
Adding AI to abas ERP means reading through OpenAccess, the ODBC/JDBC-compliant layer abas exposes over its proprietary database, and grounding a model on that data alongside documentation of your Business Extensions and FOP (Field Oriented Programming) customizations. abas shops are typically make-to-order or engineer-to-order manufacturers with lean IT teams, so the highest-value first use case is usually quote and order data, not a general-purpose chatbot.
Read the guideproALPHA + private AIAI for proALPHA: grounded answers that respect TISAX-level controls
Adding AI to proALPHA means building read access into its PPS, MRP, and APS modules through its API and web services layer, then grounding a model on that data with the same information-security discipline automotive OEMs already expect of their suppliers under TISAX. proALPHA's customer base is heavily German Mittelstand automotive and machinery suppliers, so data residency and audit-trail integrity matter as much as the AI itself.
Read the guideTOTVS Protheus + private AIAI for TOTVS Protheus: grounded answers inside your own data boundary
Adding AI to TOTVS Protheus means reading through its Framework REST API or a database replica, mapping AdvPL customizations and Brazil's fiscal compliance stack (SPED, NF-e, ICMS) into a documented semantic layer, and grounding a model on that data under LGPD-aware controls. TOTVS is Latin America's largest ERP vendor, and most of its manufacturing and distribution customers carry years of bespoke AdvPL code that any AI layer has to work with rather than around.
Read the guideMade2Manage + private AIAI for Aptean Made2Manage: A Private Assistant on Top of Your M2M Data
Aptean Made2Manage runs on SQL Server with a fairly open schema, which makes it a good candidate for a grounded AI layer: a private model reads order, job, inventory, and MRP tables through read-only views and answers the questions your Crystal Reports and ad hoc SQL currently exist to answer, without sending shop data to a public AI service. This suits the small and mid-size make-to-order and mixed-mode manufacturers who run M2M and cannot justify a large BI or AI vendor engagement.
Read the guideIntuitive ERP + private AIAI for Aptean Intuitive ERP: A Private Assistant Without a Platform Change
Aptean Intuitive ERP customers, many of them small and mid-size discrete manufacturers in electronics and medical device production, can add AI without moving off the platform: a private model reads the Intuitive database through read-only views and answers order, engineering change, and shop floor questions in plain language, with the underlying data shown alongside every answer and nothing sent to a public AI service.
Read the guideRoss ERP + private AIAI for Aptean Ross ERP: Grounded AI for Formula-Based Process Manufacturing
Aptean Ross ERP customers, food and beverage, chemical, and life sciences process manufacturers running formula-based BOMs, lot genealogy, and catch weight, can add AI by grounding a private model in their Ross data: lot traceability questions, formula and recipe variance checks, and quality hold reviews answered in plain language, without sending formulation or supplier data to a public AI service.
Read the guideVisibility ERP + private AIAI for Visibility ERP: On-Prem AI for ETO Aerospace and Electronics Manufacturers
Visibility ERP, Aptean's engineer-to-order and configure-to-order system built on Progress OpenEdge and used heavily in aerospace, defense, and electronics manufacturing, holds exactly the project, configuration, and traceability data that AI works best on. A private model grounded in Visibility's project, contract, and MRP tables answers order status, configuration impact, and shortage questions in plain language, running on infrastructure the company controls so ITAR and export-controlled technical data never has to leave the building.
Read the guideGLOVIA G2 + on-prem AIAI for Fujitsu GLOVIA G2 that stays inside your contract boundary
AI on Fujitsu GLOVIA G2 works best as a private layer that reads order management, configurable BOM, and engineering change data through GLOVIA's own APIs, then answers questions and drafts routine transactions without any contract or technical data leaving the customer's network. For most GLOVIA G2 shops running government or prime-contractor work, that means a self-hosted model, not a vendor's shared cloud AI service.
Read the guideRamco Aviation Suite + on-prem AIAI for Ramco Aviation that respects airworthiness and data boundaries
AI on Ramco Aviation Suite works by reading maintenance program, work package, component, and records data through Ramco's own APIs and VirtualWorks platform, then answering questions and drafting routine documentation without airline or MRO data leaving the operator's control. For carriers and MROs bound by EASA Part-M/145 or FAA Part 121/145 recordkeeping, that means a private, auditable layer, not a public AI chatbot.
Read the guideSwiss-AS AMOS + on-prem AIAI for AMOS that keeps technical records and CAMO data on your side of the fence
AI on AMOS works by reading maintenance, engineering, materials, and CAMO data through AMOS's XML interfaces and web services, then answering questions and drafting routine documentation without technical records leaving the airline or MRO's control. For CAMOs and Part-145 organizations answerable to EASA or FAA oversight, that means a private, auditable layer, not AMOSweb calls routed through a public AI API.
Read the guideQuantum Control + on-prem AIAI for Quantum Control that keeps traceability and pricing data on your side
AI on Quantum Control works by reading inventory, tag and certificate data, repair order, and quoting records through Component Control's APIs, then answering questions and drafting routine documents without traceability records or pricing data leaving the operator's control. For parts traders and repair stations where an 8130-3 or EASA Form 1 mismatch is a serious problem, that means a private, source-cited layer, not a public AI chatbot.
Read the guideTRAX eMRO + private AIAI for TRAX eMRO That Never Leaves Your Maintenance Records Off-Site
AI for TRAX eMRO means grounding a private LLM on your eMRO database, technical records, and AD/SB library so planners and technical records staff can ask questions in plain English and get answers traceable back to the work order, component, or manual page - with nothing leaving your network.
Read the guideRusada ENVISION + private AIAI for Rusada ENVISION Without Sending Maintenance Data Off Your Network
AI for Rusada ENVISION means grounding a private LLM on ENVISION's Maintenance & Engineering and Materials & Logistics data so planners, engineers, and compliance staff can ask questions in plain English and get traceable answers - deployable fully air-gapped for military and defense aviation customers.
Read the guideCAMP / Corridor + private AIAI for CAMP Systems and Corridor Without Handing Maintenance Data to a Cloud API
AI for CAMP Systems and Corridor means grounding a private LLM on maintenance tracking, engine trend, and MRO shop data so directors of maintenance, technical records staff, and shop planners can ask questions in plain English and get answers traced back to the AD, logbook entry, or work order they came from.
Read the guideUnit4 + private AIAI for Unit4 ERP That Keeps Financial and Grant Data Under Your Control
AI for Unit4 ERP means grounding a private LLM on your Unit4 ERPx or Business World financials, project, and HR data so finance, project, and grant teams can ask questions in plain English and get answers traceable to the ledger line, project, or requisition, without routing sensitive data through a public assistant.
Read the guideDeacom ERP + private AIAI for Deacom ERP: Answers Grounded in Your Formulas, Lots, and EDI Data
Deacom keeps financials, formulation, lot genealogy, and EDI inside one SQL Server database, which makes it a good foundation for grounded AI rather than a bolt-on chatbot. A private model reading that database can answer plain-language questions about formulas, lot traceability, and order status without your recipes or costs leaving your network. This page covers where AI fits on Deacom, how to connect it safely, and what to ask before you buy.
Read the guideSYSPRO shop floor + AIAI for SYSPRO Manufacturing Operations: From Work Centers to Answers
SYSPRO's Manufacturing Operations Management, factory scheduling, and shop floor data collection generate a steady stream of work center, routing, and WIP data that planners still interpret by hand. A private AI layer grounded in that data can explain why a job slipped, what an engineering change affects, and where cost variance came from, without SYSPRO's routing and costing data ever reaching a public model.
Read the guideWorkday Financials + on-prem AIAI for Workday Financials in Manufacturing: Keeping the Plant-Floor Boundary
Manufacturers running Workday Financial Management for corporate accounting while keeping a separate plant-floor ERP for production face a specific gap: nothing joins Workday's ledger to plant operational data in plain language. Because Workday is SaaS-only, private AI here means controlling where the model and any extracted data live, not where Workday runs. This page covers that architecture honestly, including what it can and cannot do.
Read the guideKinaxis concurrent planning + private AIAI for Kinaxis RapidResponse: Explaining Plans Across Every Connected ERP
Kinaxis RapidResponse, rebranded as Kinaxis Maestro, sits above one or more transactional ERPs to run concurrent planning, and it already surfaces exceptions well. What it does not always do is explain why in terms that trace back to the underlying ERP record. A private AI layer grounded in both Kinaxis and the connected ERPs can close that gap without sending planning or program data to a public model.
Read the guidePlex + automotive suppliersAI for Plex in Automotive Supply: Answers From Your Own Releases, Control Plans and Containment Records
AI for Plex in an automotive plant puts a private language model on top of Plex production, quality and shipping data so planners, quality engineers and plant managers can ask plain-English questions about customer releases, control plan deviations, containment status and premium freight, with the underlying query shown. Because Plex is SaaS, the AI layer reads through Plex APIs into a customer-controlled store, stays read-only by default, and keeps IATF 16949 evidence questions answerable in minutes instead of days.
Read the guideQAD + automotive suppliersAI for QAD in Automotive: Schedules, Traceability and IATF Evidence From Your Own OpenEdge Data
AI for QAD in automotive connects a private language model to QAD Adaptive ERP so supply chain, quality and plant teams can ask plain-English questions about customer schedule releases, MRP, lot and serial traceability, supplier performance and IATF 16949 evidence, with the underlying query shown. It reads through the OpenEdge SQL layer or QAD's integration services, stays read-only by default, and runs on-prem or in a private cloud tenancy.
Read the guideDELMIAworks + injection moldingAI for DELMIAworks in Injection Molding: Resin, Molds, Presses and Scrap From Your Own Data
AI for DELMIAworks in an injection molding plant connects a private language model to production, mold, resin and quality data so planners, process engineers and plant managers can ask plain-English questions about press scheduling, cycle-time variance, regrind use, mold maintenance and scrap, with the underlying query shown. It reads the DELMIAworks data read-only, runs on-prem or in a private cloud, and keeps customer part and pricing data inside your boundary.
Read the guideQAD + medical devicesAI for QAD in Medical Devices: Traceability, DHR Review and CAPA Trends Without Exposing Regulated Data
AI for QAD in a medical device company puts a private, read-only language model on QAD Adaptive ERP so quality, regulatory and operations teams can ask plain-English questions about lot and serial traceability, device history record completeness, supplier controls and CAPA and complaint trends, with the query shown. It runs on-prem or in a private tenancy and is designed to support, not replace, a validated quality system under FDA QMSR and ISO 13485.
Read the guidePlex + food and beverageAI for Plex in Food and Beverage: Lot Genealogy, Allergen Control and Recall Readiness From Your Own Data
AI for Plex in a food and beverage plant puts a private language model on top of Plex lot, production, quality and shipping data so food safety, quality and operations staff can ask plain-English questions about lot genealogy, expiry, allergen changeovers, supplier approvals and mock-recall readiness, with the query shown. It reads through Plex APIs into a store you control, stays read-only, and supports FSMA 204 traceability and GFSI audit preparation.
Read the guideSYSPRO + food safety AIAI for SYSPRO in Food and Beverage: Lot Trace, Expiry and Yield Answers Without Leaving Your Network
AI for SYSPRO in food and beverage connects a private language model to SYSPRO's SQL Server database and e.net business objects so quality, planning and plant teams can ask lot trace, expiry, allocation and yield questions in plain English and get answers grounded in live data. It runs read-only by default, shows the query it ran, respects SYSPRO operator roles and can be hosted on your own servers so recipes and supplier terms stay inside your network.
Read the guideSage X3 + food traceability AIAI for Sage X3 in Food and Beverage: Lot Genealogy and Shelf-Life Answers From Your Own Data
AI for Sage X3 in food and beverage lets quality, planning and customer service teams ask questions about lots, shelf life, allergens and batch yield in plain English, with answers drawn from X3's stock, lot and manufacturing tables or its web services. A private model runs on your infrastructure, shows the query it used and honours X3 roles, so formulations and customer terms are not sent to a public AI service.
Read the guideSage X3 + chemical process AIAI for Sage X3 in Chemicals: Formula, Batch and Regulatory Questions Answered Privately
AI for Sage X3 in chemicals gives formulation, quality and compliance teams a private natural-language interface to X3 data: batch genealogy, formula versions, lot quality results, hazard classification fields and shipping documents. It runs on your own infrastructure, shows the query behind each answer, honours X3 roles and keeps proprietary formulations out of public AI services.
Read the guideAcumatica + fabrication AIAI for Acumatica in Metal Fabrication: Heat Lots, Remnants and Routing Answers From Live Data
AI for Acumatica in metal fabrication uses Acumatica's contract-based REST API and OData generic inquiries to let estimators, planners and quality staff ask about heat-lot traceability, remnant stock, routing times and job costs in plain English. A private model reads under an Acumatica role, shows the data source for each answer and can run on your own servers beside a cloud ERP.
Read the guideSage 100 + small shop AIAI for Sage 100 Small Manufacturers: Practical Answers From Work Orders, BOMs and Inventory
AI for Sage 100 small manufacturers connects a private model, via read-only ODBC or the SQL database in Sage 100 Premium, to work orders, bills of material, inventory and sales orders so owners, planners and buyers can ask shortage, cost and delivery questions in plain English. It runs on a modest local machine or private cloud, so data stays in the company, and it is honest about when Excel or a standard report is enough.
Read the guideJobBOSS2 + precision machiningAI for JobBOSS2 in Precision Machining: Quotes, Routings and Job Cost Answers from Your Own Data
AI for a JobBOSS2 precision machine shop connects a private model to the shop's quote, job, routing and labor history so estimators, schedulers and owners can ask plain-English questions such as which part families run over estimate and why. It runs read-only on your network, shows the query behind each answer, and keeps customer drawings and pricing out of public AI services.
Read the guideE2 Shop System + metal fabricationAI for E2 Shop System in Metal Fabrication: Quote History, Material Yield and Delivery Risk
AI for an E2 Shop System metal fabricator reads the shop's estimates, orders, jobs, material usage and shipping history so estimators and managers can ask questions like which sheet-metal jobs lost money to scrap or outside finishing. It runs read-only on your network, shows the query for every answer, and keeps customer drawings and pricing off public AI services.
Read the guideGlobal Shop + industrial equipment OEMsAI for Global Shop Solutions in Industrial Equipment Manufacturing
AI for a Global Shop Solutions equipment manufacturer connects a private model to the BOMs, jobs, purchase orders and project costs the ERP already holds, so planners and project managers can ask which builds are at risk from long-lead parts or engineering changes. It runs read-only on your network and shows the query behind each answer.
Read the guideProShop + medical device machiningAI for ProShop ERP in Medical Device Machining: Traceability, NCRs and Audit Readiness
AI for a ProShop medical device machining shop reads work orders, NCRs, CAPAs, tool and gauge records and lot data so quality managers can answer traceability and audit questions in plain English. Because ProShop is cloud-hosted, data is pulled through its API into a model you control, read-only, with every answer tied to a source record.
Read the guideAcumatica + supply AIAI for Acumatica Inventory and Purchasing: Stock, Reorder and Vendor Questions Answered
AI for Acumatica inventory and purchasing answers plain-English questions about stock levels, reorder risk, open purchase orders, receipts and vendor lead times by reading Acumatica through its contract-based REST API or generic inquiries. It is read-only by default, shows the query, and can run on infrastructure you control.
Read the guideOdoo MRP + private AIAI for Odoo Manufacturing (MRP): BOMs, Work Orders and Shortages
AI on Odoo Manufacturing reads manufacturing orders, BOMs, work orders and stock moves through the external API or a read replica, then answers questions such as which orders will miss their date because a component is short. It proposes changes for a planner to approve and does not write to Odoo on its own.
Read the guideOdoo Purchase + Inventory AIAI for Odoo Purchasing and Inventory: Reorder, Receive, Reconcile
AI for Odoo Purchase and Inventory reads purchase orders, supplier info, stock quants and moves to tune reordering rules, flag late suppliers and surface stock discrepancies. Buyers and warehouse staff get answers in plain language and approve any change before it reaches Odoo.
Read the guideQAD supply chain + AIAI for QAD Supply Chain: Schedules, Suppliers and MRP Exceptions
AI for QAD supply chain reads purchase schedules, MRP output, supplier performance and inventory through approved interfaces such as QXtend or a database replica. It highlights risk, explains exceptions and drafts actions for planners and buyers, while QAD remains the system of record.
Read the guidePlex production + quality AIAI for Plex Production and Quality: Scrap, Downtime and Nonconformance
AI for Plex production and quality reads job, workcenter, scrap, downtime and quality data through Plex APIs and data sources, then finds patterns and answers questions in plain language. Plex is cloud-hosted, so the AI layer runs on your side, pulling approved data over authenticated API calls.
Read the guideSage X3 purchasing + stock AIAI for Sage X3 Purchasing and Stock: Orders, Receipts and Lots
AI for Sage X3 purchasing and stock reads purchase orders, receipts, supplier data, stock balances and lots through the X3 GraphQL or web service APIs. It reviews orders, tracks supplier delivery performance and answers stock questions, while approvals and postings stay in X3.
Read the guideSYSPRO + WIP job costingAI for SYSPRO production and job costing
AI on SYSPRO reads work-in-progress jobs, operations, labor and material postings, and compares them with the routing and bill of materials so a plant manager can ask why a job is over cost or late and get an answer tied to specific postings. It runs read-only against the SYSPRO SQL Server database or its business objects and can be hosted on-prem.
Read the guideDELMIAworks + schedulingAI for DELMIAworks production scheduling
AI on DELMIAworks reads work orders, routings, work center load, material availability, and the planning and scheduling board so a planner can ask why an order is late and what moving it would displace. It does not replace the finite scheduler; it explains its output and finds the exceptions faster. Access is read-only and can be hosted on-prem.
Read the guideJobBOSS2 + job shop AIAI for JobBOSS2 quoting and scheduling
AI on JobBOSS2 compares a new request with similar past jobs, using quoted versus actual hours, material, and outside services, and helps schedulers see why a job will miss its date. It reads JobBOSS2 data without writing to it, so the estimator and scheduler stay in control. It can be hosted privately for shops with ITAR or customer confidentiality obligations.
Read the guideGlobal Shop + schedulingAI for Global Shop Solutions scheduling
AI on Global Shop Solutions reads work orders, routing operations, work center load, and material status so a scheduler can ask why a job is late and what a rush order would push out. It explains and compares; it does not replace the finite scheduler. Access is read-only, with on-prem hosting for shops that need it.
Read the guideProShop + quality recordsAI for ProShop quality and documentation
AI on ProShop ERP reads parts, work orders, inspection plans, non-conformances, calibration, and controlled documents so a quality manager can answer audit questions and prepare first article packages from the shop's own records. Access is read-only through ProShop's API, with the AI stack hosted privately for controlled work.
Read the guideAI for Manufacturing Systems
59 guidesMES, PLM, QMS, CMMS/EAM, APS, WMS, CPQ, EDI, IIoT and BI systems that sit around the ERP.
AI for Siemens Opcenter Execution MES, Running Inside Your Own Network
AI for Siemens Opcenter Execution connects a private language model to the MES data model (lots, containers, work orders, operations, nonconformances, genealogy) through read-only database views and the product's web services, so engineers and supervisors can ask plain-English questions and get answers with the query shown. It runs on-prem next to the Opcenter database and the ERP, so traceability data never goes to a public model.
Read the guideFactoryTalk and Plex MES + AIAI for Rockwell FactoryTalk ProductionCentre and Plex MES, Without Exposing Plant Data
AI for Rockwell MES puts a private language model over FactoryTalk ProductionCentre's relational data and web services, or over Plex MES through its REST APIs, so supervisors and quality staff can ask plain-English questions about orders, downtime, genealogy and quality. The model is grounded in your records, shows its query, and does not write back without human approval.
Read the guideTulip + private AIAI for Tulip: Grounded Answers From Operator Apps, Tables and Your ERP
AI for Tulip reads operator-app data held in Tulip Tables and completion records through the Tulip API, joins it to ERP orders and materials, and lets engineers and supervisors ask plain-English questions with the source shown. Writes back to Tulip or the ERP go through Connectors and an approval step, and the model can run privately if data must stay on-prem.
Read the guideCritical Manufacturing MES + AIAI for Critical Manufacturing MES in Semiconductor, Electronics and Medical Device Plants
AI for Critical Manufacturing MES connects a private model to the MES data model of materials, lots, equipment, orders and tracking, using read-only access and the platform's integration layer, plus ERP context. Engineers and planners ask plain-English questions about WIP, equipment state, genealogy and holds, with every answer tied to a visible query. It runs on-prem or in a sovereign environment.
Read the guideAVEVA MES + private AIAI for AVEVA MES: Ask Questions Across MES, Historian and ERP Without Leaving Your Network
AI for AVEVA MES, formerly Wonderware MES, connects a private model to MES records held in SQL Server, to AVEVA Historian time-series, and to your ERP, so supervisors and quality staff can ask plain-English questions about orders, genealogy, downtime and quality with the query shown. It is read-only by default and can run on-prem or air-gapped.
Read the guideFactoryLogix + private AIAI for Aegis FactoryLogix: Ask Your Electronics MES About Lots, Lines and Escapes
AI for Aegis FactoryLogix connects a private language model to the MES database and its integration interfaces so process, quality and NPI engineers can ask plain-English questions about unit genealogy, material lots, line performance and nonconformances, with the underlying query shown. Joined to ERP data for orders, costs and suppliers, it runs on-prem or in a private cloud so traceability data for regulated electronics never leaves your boundary.
Read the guideMachineMetrics + ERP + private AIAI for MachineMetrics: Connect CNC Machine Data to ERP Jobs, Costs and Answers
AI for MachineMetrics joins the machine data it collects (cycle times, part counts, downtime reasons, feed and spindle signals) with ERP jobs, routings and costs, so operations teams can ask why a job ran long or which machines lose the most time and get a grounded, cited answer. MachineMetrics data is exported through its API into a store you control, and the AI layer can run on-prem.
Read the guideMattec MES + private AIAI for Epicor Mattec MES: Press Data, Scrap and Mold History in Plain English
AI for Epicor Mattec MES connects a private language model to the MES database so plant managers and process engineers can ask about press downtime, scrap by reason, cycle time drift and mold performance, with the query shown. Joined to ERP orders and costs, it runs on-prem next to the MES, which is often hosted on a plant SQL Server.
Read the guideIgnition + ERP + private AIAI for Ignition SCADA: Ask Tag History, Alarms and OEE Questions, Joined to the ERP
AI for Ignition connects a private language model to the Ignition tag historian, alarm journal and any SQL databases the gateway writes to, so engineers can ask about trends, alarms, downtime and production counts in plain English. Joined to ERP orders through its APIs, it runs on-prem beside the gateway and stays read-only.
Read the guideProficy + ERP + private AIAI for GE Vernova Proficy: Historian, Plant Applications and ERP Questions Answered Privately
AI for GE Vernova Proficy connects a private language model to Proficy Historian data and the Plant Applications database so engineers and production managers can ask about trends, downtime, quality events and production in plain English, with the query shown. Joined to ERP orders and lots, it runs on-prem, read-only and outside the control network.
Read the guideWindchill PLM + private AIAI for PTC Windchill That Respects Your Access Rules and Keeps Product Data Inside
AI for PTC Windchill connects a privately hosted language model to Windchill's OData REST domains and document vault so engineers, change analysts and quality staff can ask plain-English questions about parts, BOMs, change requests and CAD metadata and get answers with the source objects cited. It runs on-prem or in a private cloud, evaluates every request as the asking user, and keeps drawings and technical data inside your boundary.
Read the guide3DEXPERIENCE + private AIAI for 3DEXPERIENCE and ENOVIA That Works With Your Business Objects, Not Around Them
AI for Dassault 3DEXPERIENCE and ENOVIA places a private language model beside 3DSpace, reading parts, engineering changes and documents through supported web services under the asking user's identity. Engineers and change coordinators ask in plain English and receive answers citing the underlying business objects, while IP and controlled data stay in your environment.
Read the guideArena PLM + private AIAI for Arena PLM: Questions, Change Drafts and ERP Comparisons Without Handing Over Your BOMs
Arena PLM is a cloud-only product, so AI for Arena works by pulling entitled data through its REST API into a private retrieval and model layer that you host. Engineering and operations staff can then ask about items, BOMs, approved manufacturers and change orders, draft ECO text and compare released BOMs with the ERP, with answers citing item numbers and revisions.
Read the guideAras Innovator + private AIAI for Aras Innovator That Understands Your ItemTypes and Honors Your Permissions
AI for Aras Innovator connects a private language model to the platform's OData REST endpoint and ItemType model so engineers can ask about parts, BOMs, ECOs and documents in plain English. Each request runs as the asking user so Aras permissions apply, answers cite the item and generation, and the AI stack can sit on-prem or in your own cloud.
Read the guidePropel PLM + private AIAI for Propel PLM: Using Salesforce Data Access Properly, With Models You Control
Propel PLM runs on the Salesforce platform, so AI for Propel reads item, revision, BOM and change records through Salesforce APIs under the asking user's sharing and field-level security. A privately hosted model then answers questions and drafts change text with citations. It is useful where Salesforce's own AI features do not reach product data or where you need to control hosting.
Read the guideETQ Reliance + private AIAI for ETQ Reliance: Joining Your Quality Records to Your ERP Data
AI for ETQ Reliance reads the records quality teams already keep in ETQ (CAPA, nonconformance, complaints, audits, supplier corrective actions, controlled documents) through its REST API and joins them to ERP lots, work orders and supplier data. It answers questions such as which CAPAs trace to one supplier or lot, drafts root-cause summaries for human review, and never closes or approves a record on its own.
Read the guideMasterControl + private AIAI for MasterControl: Controlled Documents and Quality Events Joined to Your ERP
AI for MasterControl connects to the controlled documents, training records, CAPAs and manufacturing records you manage in MasterControl and joins them to ERP lots, materials and suppliers. It answers questions across SOPs and quality events with citations, drafts for human review, and leaves approvals and electronic signatures inside MasterControl, where Part 11 controls already apply.
Read the guideTrackWise Digital + private AIAI for TrackWise Digital: Complaints, Deviations and CAPA Joined to ERP Lots
TrackWise Digital runs on the Salesforce platform, so AI can read complaints, deviations, CAPAs and audits through Salesforce REST and SOQL access under a scoped integration user. A private layer then joins those records to ERP lots, suppliers and work orders, drafts investigation summaries with citations, and leaves record approval and e-signature to people inside TrackWise.
Read the guideComplianceQuest + private AIAI for ComplianceQuest: Connecting QMS Records to ERP, Suppliers and Shop Floor
ComplianceQuest is built on the Salesforce platform, so its QMS, supplier quality and related records can be read through Salesforce APIs under a scoped integration user. A private AI layer joins those records to ERP data, drafts CAPA and supplier summaries with citations, and leaves approvals, signatures and closure to people inside ComplianceQuest.
Read the guide1factory + private AIAI for 1factory: First Article and Inspection Data Joined to Your ERP
AI for 1factory works on the inspection side of quality: ballooned drawings, characteristics, first article inspection reports (AS9102 style), and measured results. It joins those records to ERP work orders, lots and suppliers, flags out-of-tolerance trends and missing characteristics, and drafts summaries for quality engineers, while the inspection record and sign-off remain in 1factory.
Read the guideIBM Maximo + private AIAI for IBM Maximo That Reads Your Work Orders and Failure History Without Leaving Your Boundary
AI for IBM Maximo connects a private language model to Maximo's REST and integration framework so planners, reliability engineers and technicians can ask plain-English questions about WORKORDER, ASSET and failure data and get answers grounded in live records. It runs on-prem or in a sovereign cloud next to Maximo Manage or a 7.6.1.x install, and any write-back is drafted for human approval.
Read the guideFiix CMMS + private AIAI for Fiix CMMS: Ask Questions Across Work Orders, Assets and Parts Using Your Own Data Boundary
AI for Fiix CMMS uses the Fiix API to let maintenance managers, planners and technicians ask plain-English questions about work orders, assets, parts and meters and get answers grounded in live data. Because Fiix is a cloud-only product, the private layer pulls only the fields it needs into an environment you control and connects maintenance history to your ERP and documents.
Read the guideUpKeep CMMS + private AIAI for UpKeep CMMS: Turn Work Order History Into Answers Without Handing It to a Public Model
AI for UpKeep uses the UpKeep REST API to let small and mid-size maintenance teams ask plain-English questions about work orders, assets, parts and preventive maintenance, grounded in live records. A private layer keeps the synchronized copy, index and model under your control, and links UpKeep with ERP cost and purchasing when you need it.
Read the guideeMaint CMMS + private AIAI for eMaint CMMS: Reliability Questions Answered From Your Own Maintenance History
AI for eMaint connects a private language model to eMaint data so reliability engineers and maintenance planners can ask plain-English questions about work orders, equipment, PM schedules and parts, grounded in live records. Because eMaint belongs to the Fluke Reliability portfolio, it can also bring in condition monitoring context, and join with ERP cost data inside a boundary you control.
Read the guideHxGN EAM + private AIAI for HxGN EAM, Formerly Infor EAM: Ask Your Asset Records Questions Without Exporting Them
AI for HxGN EAM connects a private language model to the EAM web services and database so asset managers, planners and technicians can ask plain-English questions about equipment, work orders, stores and purchasing and get answers grounded in live records. It runs on-prem or in a sovereign cloud, and fits sites that came from Infor EAM or Datastream 7i and still run the same data model.
Read the guidePlanetTogether + private AIAI for PlanetTogether APS: Explain the Schedule, Compare Scenarios, Reconcile with the ERP
AI for PlanetTogether adds a private, grounded assistant next to the APS so planners can ask why a job moved, which scenario protects a due date, and where the ERP and the schedule disagree. It reads scheduling data and ERP data through governed connectors, shows the query behind each answer, and leaves the optimizer itself and any publish step under planner control.
Read the guideOpcenter APS + Preactor + private AIAI for Siemens Opcenter APS and Preactor Installations
AI for Siemens Opcenter APS, including long-running Preactor installations, adds a private assistant that explains schedule outcomes, traces late orders to their constraint, and reconciles the schedule with ERP and MES data. It reads through governed connectors and leaves the scheduling engine and any release to the floor under planner control.
Read the guideAsprova + private AIAI for Asprova APS: Explaining the Schedule and Connecting It to Your ERP
AI for Asprova adds a private assistant that reads the scheduling project and the ERP data around it, so planners can ask why an order is late, what limited a resource, and how the plan differs from ERP records. It does not replace Asprova's scheduling logic, and any proposed change remains a draft that a planner applies.
Read the guideDELMIA Ortems + private AIAI for DELMIA Ortems: Explaining Plans and Reconciling with the ERP
AI for DELMIA Ortems adds a private assistant beside the planning and scheduling tool, so planners can ask why an order is late, which resource or material drove it, and how the plan compares with ERP records. It reads through governed, read-only connectors and does not replace the scheduling engine or release process.
Read the guideo9 Solutions + ERP-grounded AIAI Around o9 Solutions: Grounding Planning Reviews in Live ERP Data
o9 Solutions is a cloud planning platform with its own AI, so a private AI layer is not a substitute. It complements o9 by grounding plan reviews in live ERP data, reconciling o9 demand and supply plans with orders and inventory in the ERP, and keeping sensitive execution data on infrastructure you control.
Read the guideManhattan Active WMS + private AIAI for Manhattan Active WMS: Answers From Your Own Warehouse Data, Next to Your ERP
AI for Manhattan Active WMS places a privately hosted model beside the warehouse system and the ERP, reads waves, tasks, inventory and shipment data through the platform's APIs and event feeds, and answers warehouse questions in plain English with the underlying query shown. Manhattan ships its own AI inside the product, so the case for an external layer is cross-system questions that join the WMS to ERP orders, lots and supplier data.
Read the guideBlue Yonder WMS + private AIAI for Blue Yonder WMS: One Question Across the Warehouse and the ERP
AI for Blue Yonder WMS adds a privately hosted model that reads warehouse inventory, tasks and shipments from the WMS and joins them with ERP orders and lots, so supervisors and planners can ask plain-English questions and see the source query. How it connects depends on your edition: older JDA and RedPrairie lineage installations are reachable through their database and command layer, while newer deployments use the vendor's APIs and cloud services.
Read the guideInfor WMS + ION + private AIAI for Infor WMS: Warehouse Answers Joined to Your Infor ERP
AI for Infor WMS connects a private model to warehouse inventory, receipts, tasks and shipments, using ION messaging and APIs where available or a read-only database copy where the edition allows, and joins them with ERP orders from SyteLine, M3, LN or another system. Staff ask in plain English and see the source query; the WMS remains the system of record.
Read the guideKorber WMS + HighJump + private AIAI for Korber WMS and HighJump: Make a Long-Lived Warehouse System Easier to Question
AI for Korber WMS, including installations that descend from HighJump, connects a private model to warehouse inventory, tasks and shipments through a read-only database replica, the system's integration tools or scheduled extracts, and joins them with ERP data. The edition and the amount of customisation decide the path, so the first step is an inventory of what you actually run.
Read the guidePaperless Parts + private AIAI for Paperless Parts: Faster RFQ Review Without Sending Your Customers' Drawings to a Public Model
Paperless Parts is a quoting and order-management platform for custom manufacturers that builds cost-based quotes from CAD models and drawings. AI around it works best as a private layer that reads RFQ packages, past quotes and ERP actuals, flags risk on drawings and notes, and drafts review notes for the estimator, while the pricing engine and the human sign-off stay where they are.
Read the guideTacton CPQ + private AIAI for Tacton CPQ: Complement the Constraint Solver, Do Not Replace It
Tacton CPQ is a constraint-based configure-price-quote system for complex, engineered products. Generative AI should not replace its solver, because a configuration must be provably valid. It adds value around it: turning messy customer requirements into configurator inputs, explaining why a configuration is infeasible, maintaining product model documentation, and checking the ERP bill of materials handoff.
Read the guideSOLIDWORKS PDM + private AIAI for SOLIDWORKS PDM: Ask Your Vault and Your ERP the Same Question
SOLIDWORKS PDM stores CAD files, revisions, workflow states and BOMs in a vault backed by an archive server and a SQL database. AI helps most on the metadata and process around the files: finding parts and where-used, explaining BOM differences between revisions, reviewing change workflows, and checking that PDM data and the ERP item master agree.
Read the guideAutodesk Vault + private AIAI for Autodesk Vault: Item Masters, Change Orders and ERP Sync Without Public Models
Autodesk Vault Professional manages Inventor and AutoCAD files, Items, BOMs, lifecycles and change orders on a server with a SQL database. A private AI layer can search files and Items in plain language, summarize change orders, compare BOMs and check Vault Items against the ERP item master, using the Vault SDK or supported connectors and keeping design data in your environment.
Read the guideManufacturing Cloud + ERP AIAI for Salesforce Manufacturing Cloud: Sales Agreements Reconciled with ERP Actuals
Salesforce Manufacturing Cloud manages sales agreements, account-based forecasts and rebates for manufacturers that sell on repeat volume. Its weak point is the link to ERP actuals. A private AI layer can reconcile agreed versus actual volumes, explain variance, check rebate accruals and draft account plans, pulling only the needed Salesforce data through its APIs.
Read the guideSPS Commerce + ERP AIAI for SPS Commerce: EDI Order, ASN and Chargeback Questions Answered From Your ERP
AI for SPS Commerce joins the EDI documents flowing through the SPS network (850, 855, 856, 810, 997) with the sales orders, shipments and invoices in your ERP, so an EDI analyst can ask why an order failed, which ASNs will trigger a retailer chargeback, or where an invoice is stuck. It runs privately next to your ERP and reads exported or API-fed EDI data without writing back.
Read the guideTrueCommerce + private AIAI for TrueCommerce: Tracing EDI Transactions Across Partners, Maps and Your ERP
AI for TrueCommerce connects a private model to your EDI transaction history, trading partner maps and ERP order data, so staff can ask why a document failed, which partner rule broke, or whether an order made it into the ERP. It runs on your infrastructure, reads data without changing maps, and sends nothing to a public model.
Read the guideBoomi + ERP AIAI for Boomi: Process Documentation, Error Triage and Governed ERP Access
AI for Boomi uses a private model to read your AtomSphere process definitions and execution records, explain what an integration does, triage failed runs, and expose ERP data to agents through Boomi-managed APIs. Because Boomi Atoms can run on your own infrastructure, the AI can stay inside the same boundary as the data.
Read the guideMuleSoft + ERP AIAI for MuleSoft in Manufacturing: System APIs, DataWeave and ERP Agents Under Governance
AI for MuleSoft layers a private model over Anypoint assets, logs and API specifications to explain flows and DataWeave, triage runtime errors and let agents call ERP data through governed System and Process APIs. It can run on your infrastructure alongside Runtime Fabric or on-prem Mule runtimes.
Read the guideKepware + shop-floor AIAI for Kepware KEPServerEX: Linking Shop-Floor Tags to ERP Work Orders, Read-Only
AI for Kepware KEPServerEX reads machine data that the server already exposes through OPC UA, MQTT or logged SQL tables and joins it to ERP work orders, routings and downtime reasons, so engineers can ask why a line stopped or which jobs ran slow. The AI is read-only toward the plant floor and runs on-prem inside your network.
Read the guidePower BI + ERP + private AIAI for Power BI on ERP Data: Answers Beyond the Dashboard You Already Built
AI on Power BI and ERP data means a language model that reads your semantic model (tables, measures, relationships) and, where needed, the live ERP, then answers plain-English questions with the query shown. Copilot in Power BI covers cloud-hosted models; a private layer covers the cases where ERP data, row-level security or sovereignty rules keep the model on your own infrastructure.
Read the guideQlik Sense + ERP + private AIAI for Qlik on ERP Data: Ask Questions Beyond the Selections You Built
AI for Qlik on ERP data puts a language model beside your Qlik Sense apps and ERP so users can ask plain-English questions, get answers from the associative model and, when needed, the source system, with Section Access and the generated query shown. It suits client-managed Qlik Sense deployments where ERP data cannot go to a hosted model.
Read the guideTableau + ERP + private AIAI for Tableau on ERP Data: Answers From Published Sources and the ERP Behind Them
AI for Tableau on ERP data lets users ask plain-English questions of published data sources and, when the extract does not hold the answer, the ERP itself, with the generated query shown and Tableau's row-level filters respected. It is aimed at Tableau Server deployments where operational data should not go to a hosted model.
Read the guideSnowflake + ERP + AIAI for Snowflake in Manufacturing: Questions Across ERP, MES and Quality Data
Snowflake AI for manufacturing means a language model that reads ERP, MES and quality tables landed in Snowflake and answers plain-English questions with the SQL shown, under Snowflake roles. Snowflake Cortex handles this inside the platform; a private layer is for cases where data or models must stay outside a public cloud.
Read the guideDatabricks + ERP + AIAI for Databricks in Manufacturing: Lakehouse Data From ERP, MES and the Line
Databricks AI for manufacturing uses a lakehouse that holds ERP, MES and sensor data under Unity Catalog, then answers questions in plain English with the generated SQL shown. Genie spaces cover this inside Databricks; a private layer applies where models must run outside a public cloud or data cannot land there.
Read the guideServiceMax + private AIServiceMax AI Integration: Private AI Over Work Orders, Installed Base and ERP Data
AI on ServiceMax works by reading work orders, installed products, entitlements and PM plans through Salesforce APIs, joining them to ERP parts, contracts and invoices, and answering in natural language with the source records shown. It can run on open-weight models inside your own network, with write-back limited to drafts a human approves.
Read the guideSalesforce Field Service + ERP AISalesforce Field Service AI Integration: Private AI Joined to ERP Stock and Contracts
Private AI for Salesforce Field Service reads WorkOrder, ServiceAppointment, ServiceResource and Asset records through Salesforce APIs, joins them to ERP inventory, contracts and cost, and answers with the query and sources shown. It complements Salesforce's own AI where data must leave the CRM or the model must stay on your hardware.
Read the guideDynamics 365 Field Service AIDynamics 365 Field Service AI Integration: Dataverse Data Joined to Your ERP
AI for Dynamics 365 Field Service reads work orders, bookable resource bookings, agreements and customer assets from Dataverse through the Web API or Synapse Link, joins them to ERP parts, cost and invoices, and answers in natural language with sources. Microsoft's Copilot covers in-product tasks; a private layer covers cross-ERP joins and controlled deployment.
Read the guideIFS FSM + private AIIFS Field Service Management AI Integration: Private AI on IFS Cloud Service Data
AI for IFS Field Service Management reads service requests, work orders, work tasks, agreements and equipment through IFS Cloud's OData REST projections, joins them to IFS Supply Chain, Finance and Projects data in the same platform, and answers with sources. Open-weight models can run in your own environment for controlled industries.
Read the guideOracle Field Service + AIOracle Field Service AI Integration: Private AI Over Activities, Resources and Inventory
AI for Oracle Field Service reads activities, resources, inventory and service requests through the Core and Metadata REST APIs and the Data Connector extracts, joins them to Fusion, E-Business Suite, JD Edwards or other ERP data, and answers with sources shown. Models can run in your own environment with human approval on any write-back.
Read the guideInsights Hub + private AIAI for Siemens Insights Hub (MindSphere): Asset Data Joined to ERP, Answered in Plain English
AI for Siemens Insights Hub reads asset models and time series through the Insights Hub APIs, joins them to ERP work orders, routings and material data, and lets engineers ask questions in plain English with the query and source shown. For plants that cannot send process data to a public model, the language model runs in the customer's own environment while Insights Hub remains the system of record for telemetry.
Read the guideThingWorx + private AIAI for PTC ThingWorx: Natural-Language Access to Things, Value Streams and ERP Context
AI for PTC ThingWorx exposes Things, properties, services and value stream history to a private language model through the ThingWorx REST API, then joins that data to ERP production and maintenance records. Engineers ask in plain English, see the calls made, and the model can run on-prem alongside a self-hosted ThingWorx deployment.
Read the guideSiteWise + ERP + private LLMAI for AWS IoT SiteWise: Asset Models, Property Values and ERP Context in One Question
AI for AWS IoT SiteWise reads asset models and property values through the SiteWise APIs, joins them with ERP orders and maintenance records, and answers plant questions in plain English with the queries shown. The model can be hosted inside the customer's AWS account or on-prem, so the choice of a managed AWS model service versus an open-weight model stays with the customer.
Read the guideLitmus Edge + on-prem AIAI for Litmus Edge: Machine Data Collected Locally, Answered Alongside Your ERP
AI for Litmus Edge uses the edge platform's normalised device data, whether from OPC UA, Modbus, Siemens S7 or EtherNet/IP sources, and joins it with ERP orders and maintenance records so engineers can ask questions in plain English. Because Litmus Edge runs on the plant network, the whole stack including the language model can stay on-prem and offline-capable.
Read the guideInfinityQS SPC + ERPAI for InfinityQS SPC: Explain Out-of-Control Signals Using ERP Lots, Orders and Suppliers
AI for InfinityQS reads SPC characteristics, subgroups, control chart signals and capability indices, and joins them to ERP lots, production orders and supplier receipts so quality engineers can see what changed when a chart goes out of control. A private model runs in the customer's environment, reads data under existing roles, and cites the records behind each answer.
Read the guideAcross every ERP
60 guidesUse cases, industries, compliance regimes, and buying guides that apply across ERPs.
AI Across PLM and ERP: Teamcenter, Windchill, and Your ERP
You add AI across your PLM and ERP by grounding a private model on both Teamcenter or Windchill (design BOM, ECOs, CAD metadata) and your ERP (manufacturing BOM, open orders, routings), so an engineer can ask 'what does this ECO break downstream' and get an answer that spans both systems instead of two separate logins. The mechanism is retrieval and structured queries over both systems' change and BOM objects; it does not replace the PLM-to-ERP integration you already have, it answers the questions that integration was never built to answer directly.
Read the guideAgents + approval gatesAI Agents for ERP, Running On-Prem
An AI agent for ERP is a model given a defined set of tools, read queries, draft actions, and specific write actions gated by human approval, that it can call against your ERP to complete a task like drafting a PO follow-up or triaging a scheduling exception. Run on-prem, the model and its tool access stay inside your network, and the key design decision is not which model to use, it is which actions the agent is allowed to take on its own versus which require a person to approve first. Most production deployments start read-only and add specific, narrow write actions only after the read-only version has proven reliable.
Read the guideRAG + SQL + permissionsA Private LLM Grounded on Your ERP Data
A private LLM for ERP data is a language model served on infrastructure you control, connected to your ERP through two complementary mechanisms: retrieval-augmented generation (RAG) over documents and unstructured content, and structured querying, often called text-to-SQL, over transactional tables. The model never trains on your data and never sends it to a third-party API; the harder engineering problem is making sure the model only ever sees, and only ever answers with, data the asking user is actually permitted to see in the ERP itself.
Read the guideAsk your ERP anythingNatural Language Query for ERP Data: Ask SAP, Infor, or Oracle a Question in Plain English
Natural language query over ERP data works by translating a question into a governed SQL or API call against your SAP, Infor, Oracle, or NetSuite database, then returning the result with the underlying query shown. Done well, it runs read-only, respects existing ERP roles table by table, and never lets a model touch production data unsupervised. Netray builds this as ERPray, deployed on-prem or in a private cloud so the questions and the data never leave your boundary.
Read the guideForecasting plus a reason whyAI Demand Forecasting for Manufacturers: Better Numbers and an Explanation Planners Can Trust
AI-assisted demand forecasting layers a statistical or machine learning forecast on top of your ERP's own demand history, whether that is SAP IBP/APO, SyteLine MRP, Infor M3 MEC, or Oracle demand planning, then uses an LLM to explain the forecast in plain language: what drove the change, which SKUs are outliers, and what assumptions were used. The forecast still runs against your data on your infrastructure. Netray builds this as a use case within ERPray or a custom build, not as a replacement for your planning engine.
Read the guidePO follow-up that does not wait on a personAI Purchase Order Automation: Supplier Follow-Up and Expedite Without a Buyer Chasing Email
AI purchase order automation reads open PO lines directly from your ERP, whether SAP EKKO/EKPO, SyteLine po_ohdr/po_itemw, or Oracle PO_HEADERS_ALL, drafts and sends supplier follow-up for confirmations and late lines, and flags exceptions for a buyer instead of quietly rescheduling anything. It does not place or change a PO on its own. Netray builds this as an agent with a human approval gate on every write-back, running on-prem or in a private cloud beside your ERP.
Read the guideFaster paperwork, same standardAI for NCR, CAPA, and 8D Drafting in AS9100 and ISO 9001 Quality Systems
AI drafting for nonconformance reports, corrective action plans, and 8D reports pulls the facts already in your ERP, such as SyteLine QCS nonconformance records, SAP QM notifications, or Infor LN quality sessions, and produces a structured first draft for the quality engineer to review and finalize, not a system that closes an NCR on its own. It cuts drafting time on routine nonconformances while keeping AS9100 and ISO 9001 sign-off exactly where it already sits, with the quality engineer. Netray deploys this on-prem or in a private cloud so nonconformance detail involving customer or program data never leaves your network.
Read the guideKnow what an ECO actually touchesAI for Engineering Change Impact on BOMs, Routings, and Open Orders
AI-assisted engineering change impact analysis reads a proposed ECO or ECN against your ERP's live BOM, routing, and open order data, such as SAP CS02 or STPO BOM tables, SyteLine im_bom, or Infor LN item-BOM structures, and produces a plain-language impact summary: which open work orders, purchase orders, and inventory the change touches, before engineering releases it. It does not release the change itself. Netray builds this on-prem or in a private cloud, since BOM and routing data on active programs is usually the most sensitive dataset in the ERP.
Read the guideAP automation + on-prem AIAI for accounts payable invoice matching in your ERP
AI for AP invoice matching combines document extraction with your ERP's own PO, receipt, and tolerance data to auto-post clean 3-way matches and hand your AP team only the exceptions that actually need a human. Run on-prem, the invoice images and vendor data never leave your network, and every auto-posted match is fully explainable against the ERP records it used.
Read the guideMRO technical records + on-prem AIAI for aviation MRO maintenance records and technical documentation
AI for aviation MRO maintenance records reads airworthiness directives, service bulletins, and logbook entries, cross-references them against open work orders and part history in your MRO ERP, and drafts the compliance narrative a records clerk or DER would otherwise write by hand. Run on-prem, technical records and configuration data stay inside your certificated facility's own network boundary.
Read the guideERP migration + on-prem AIAI-assisted data migration for ERP implementations
AI-assisted ERP data migration uses a language model to profile legacy data, propose field-level mapping rules between source and target ERP schemas, and flag likely cleansing issues, duplicate customers, inconsistent units of measure, orphaned BOM records, before they reach the target system. A migration analyst reviews and approves every rule; the AI's job is to surface the pattern faster than manual profiling would, not to make the cutover decision.
Read the guideShop floor + on-prem AIAI shop floor assistant for operators working inside your ERP
An AI shop floor assistant lets an operator ask a plain-language question, what is the next operation on this traveler, where is the current revision of this work instruction, and get an answer grounded in your ERP's live routing, BOM, and document data, without navigating ERP transaction screens built for planners, not operators. It runs on-prem, respects your ERP's existing role and plant permissions, and shows the record it pulled the answer from.
Read the guideRFQ-to-quote + on-prem AIAI for quote-to-cash in manufacturing and job shop ERPs
AI for manufacturing quote-to-cash reads incoming RFQ packages, drawings, specs, quantities, matches them against similar parts your shop has quoted before using your ERP's own job cost and pricing history, and drafts a starting quote for an estimator to review. It runs on-prem so customer RFQ data and your historical cost structure never leave your network, and every draft quote shows the historical jobs it was based on.
Read the guideITAR + on-prem AIITAR Compliant AI for ERP Technical Data
Adding AI to an ERP that stores ITAR-controlled technical data means keeping every model call, embedding, and log entry inside a boundary that only US persons can reach - a public LLM API almost never satisfies that. The fix is an on-prem or customer-controlled private cloud stack where the model, the vector index, and the ERP connector all sit inside your existing Technology Control Plan, with access mapped to citizenship and need-to-know, not bolted on afterward. Done this way, AI can search technical manuals, draft engineering change summaries, and answer questions against BOM and routing data without a single byte leaving the boundary the Empowered Official already controls.
Read the guideCMMC 2.0 + on-prem AICMMC Level 2 AI for ERP Without Blowing Up Your Scope
AI added to an ERP that stores Controlled Unclassified Information either lives inside your CMMC assessment boundary or it becomes a new, unassessed path into CUI - there is no third option. The workable pattern is deploying the model, retrieval index, and ERP connector on infrastructure already inside your Level 2 enclave, or on a clearly segregated extension of it, so the System Security Plan needs an update rather than a rewrite. Built this way, AI can draft control narratives, summarize access logs, and answer questions against ERP data without adding a single new external dependency for your C3PAO to scope.
Read the guideDFARS 7012 + NIST 800-171Mapping DFARS 7012 and NIST 800-171 Controls to AI on Your ERP
DFARS 252.204-7012 requires adequate security for Covered Defense Information under NIST SP 800-171, and that obligation does not pause for AI - any AI system touching CDI in your ERP has to be mapped, control by control, the same way every other in-scope system is. The practical path is designing the AI stack against the specific control families (AC, AU, IA, SC, SI, CM) from day one, so a Compliance Manager can produce control-by-control evidence instead of a vendor's generic security claims. That evidence also has to cover the 72-hour DoD cyber incident reporting clock if the AI system ever touches an incident involving CDI.
Read the guideDefense contractor AIAI for ERP Across the US Defense Supply Chain
The US defense supply chain runs on a patchwork of ERPs - SAP at primes, Infor LN or SyteLine at mid-tier manufacturers, Deltek Costpoint for government accounting, Oracle EBS or Epicor at smaller suppliers - and a CIO adding AI has to make it work across that patchwork without introducing a new ITAR, CMMC, or DFARS gap. The workable approach starts with one or two bounded use cases inside a private or air-gapped deployment, proves the control story with the compliance team, then extends across ERPs and facilities using the same governed architecture rather than a different tool for each system.
Read the guideAS9100D + on-prem AIAI for AS9100 Quality Management on Your ERP
For an AS9100D-certified supplier, the appeal of AI in the quality module comes with a specific condition: every drafted NCR, CAPA, or FAI summary has to trace back to a source ERP or inspection record an auditor can independently verify, or it is not usable. The practical pattern is grounding AI drafting tools directly in ERP quality data (inspection lots, NCR history, BOM and routing) with full logging of what the model read and generated, so the output speeds up documentation without becoming a black box a QMS auditor cannot follow. Deployed on-prem or in a controlled private environment, this also keeps ITAR-adjacent technical data inside the same boundary your quality and export control programs already manage.
Read the guideEMS / PCBA + on-prem AIAI for EMS and PCBA Manufacturers Running SyteLine, Epicor, or NetSuite
Electronics manufacturing services shops add AI to their ERP by grounding a private model in the item master, BOM, and AVL/AML tables already inside SyteLine, CloudSuite Industrial, Epicor Kinetic, or NetSuite, then using it to scrub incoming BOMs, flag obsolete or single-sourced parts, and draft NPI quotes from comparable historical jobs. The model runs on the manufacturer's own infrastructure so customer BOMs, pricing, and design data never leave the plant. Human buyers and estimators review every suggestion before it touches a purchase order or a quote.
Read the guideGovCon accounting + DCAA + on-prem AIAI for Government Contractor ERP Under DCAA Scrutiny
Government contractors add AI to a DCAA-compliant accounting system, typically Deltek Costpoint or a GovCon-configured SAP, Oracle, Infor, or Dynamics instance, by grounding a private model in project ledger, timekeeping, and indirect rate pool data to draft incurred cost narratives, flag potential FAR Part 31 unallowable costs, and assemble audit support packages faster. Every draft is reviewed by finance staff before it goes into a submission, and the model runs on infrastructure the contractor controls so CUI-adjacent cost and contract data never leaves the accounting system's existing security boundary.
Read the guideBuyer guide: cloud AI vs private AIFedRAMP / GCC High AI vs On-Prem AI for Your ERP: An Honest Comparison
FedRAMP High or GCC High authorization tells you a cloud service's security controls have been assessed to a federal baseline, it does not by itself resolve ITAR deemed-export risk, CUI handling scope, or where your ERP's underlying data and embeddings physically sit. On-prem or private-cloud AI grounded in the ERP removes that ambiguity by keeping data inside a boundary the organization already controls, at the cost of owning more of the deployment. The right answer usually depends on data classification, not on which option sounds more compliant.
Read the guideCIO playbook: strategy, budget, orgA CIO's Guide to Sequencing AI on Your Manufacturing ERP
A manufacturing CIO adds AI to the ERP by picking one or two high-frequency, low-risk use cases first (exception triage, natural-language reporting), grounding a model in the ERP's own data rather than a generic chatbot, and deciding upfront whether budget, ownership, and governance sit in IT or with the business unit. The sequence matters more than the technology choice: shops that start with a narrow, well-governed pilot reach production faster than shops that run five parallel proofs of concept.
Read the guideCFO + finance close + on-prem AIAI for the ERP Month-End Close: Variance Commentary Without the All-Nighter
CFOs add AI to the ERP close process by grounding a private model in the general ledger and sub-ledger data already in SAP, Oracle EBS, Infor LN, or NetSuite, using it to draft variance commentary, flag unusual journal entries, and assemble audit support documents faster. The model never posts a journal entry or finalizes a number on its own, every draft goes through the same review and approval chain the close process already uses, which is what keeps the control environment intact while cutting the mechanical work out of a compressed close calendar.
Read the guideERP AI Buyer GuideHow to Choose an ERP AI Implementation Partner
Choosing an ERP AI implementation partner comes down to five checks: do they know your ERP's actual data model, will the architecture keep sensitive data inside your boundary, can they demonstrate a working query against a real schema rather than a slide, what does the pricing model actually charge for, and who owns the system when the engagement ends. This guide gives CIOs a structured scorecard for evaluating any vendor, Netray included, against those five checks.
Read the guideERP AI Cost GuideWhat ERP AI Actually Costs: A CFO's Guide
The cost of ERP AI breaks into four components: one-time hardware (if self-hosting), integration and connector build, the model itself (open-weight models are typically free to license but not free to run), and an ongoing run-rate covering infrastructure, support, and internal staff time. This guide gives CFOs realistic ranges and the mechanisms that drive them, rather than a single number that does not fit your deployment.
Read the guideVendor copilots vs private AIBuild vs Buy: Should You Use Your ERP Vendor's AI Copilot, or Build Your Own?
Vendor copilots (SAP Joule, Microsoft Copilot for Dynamics 365, Infor GenAI/Coleman, Oracle AI Agent Studio) are fastest to turn on but tie you to the vendor's cloud tier, data residency terms, and roadmap. A private LLM grounded on your ERP data via RAG and read-only query tools gives you control over where data goes and which customizations it understands, at the cost of an integration project. The right answer usually depends on whether your data can legally and commercially sit in the vendor's cloud, and whether your value is in standard transactions or in your own custom objects and workflows.
Read the guideERP AI readinessIs Your ERP Ready for AI? A Readiness Assessment Checklist
ERP AI readiness comes down to four things: master data clean enough to trust an answer built on it, access paths (API or read replica) that do not compromise production performance or security, enough GPU capacity sized to the actual model and user count, and a governance process that defines who approves AI-initiated writes. Most stalled ERP AI projects fail on one of these four, not on model quality.
Read the guide90-day ERP AI pilotA 90-Day ERP AI Pilot Plan With Real Success Criteria
A well-scoped ERP AI pilot fits in 90 days: two weeks to scope one use case against one ERP module, four to six weeks to build and ground it against real data, three to four weeks for real users to test it against defined accuracy and adoption targets, and a final decision week. The plan below gives week-by-week milestones and the specific success criteria, not vague ones, that turn a pilot into either a funded production rollout or an honest no-go.
Read the guideERP AI business caseBuilding the ROI Business Case for ERP AI in Manufacturing
A defensible ERP AI business case rests on named benefit mechanisms tied to specific, measurable ERP transactions, PO follow-up time, invoice exception resolution, NCR drafting time, not on a general productivity percentage. Costs should be split into a one-time build/integration cost and an ongoing run cost (infrastructure, model, maintenance), compared against a clear baseline, with a sensitivity range rather than a single point estimate, because both benefit realization and adoption will vary.
Read the guideOn-prem AI, any ERP, A&DOn-Prem AI for ERP in Aerospace, Defense, and Electronics Manufacturing
Aerospace, defense, and electronics manufacturers typically cannot send ERP data to a public cloud AI service without risking a deemed export under ITAR/EAR or expanding their CMMC assessment boundary. On-prem AI, an open-weight model served on customer-owned GPUs inside the existing network boundary, avoids that risk by keeping data, prompts, and model weights inside the facility, whether the underlying ERP is SAP, Infor LN, Costpoint, IFS, or Oracle EBS.
Read the guideGMP + private ERP AIAI for ERP in Pharmaceutical Manufacturing Under GMP
AI for a pharmaceutical ERP means grounded, read-only answers over batch records, quality inspection lots, deviations and stock status, served from infrastructure inside your validated environment. It speeds up batch genealogy, release-readiness checks and deviation triage while the qualified person, QA head and ERP roles keep every decision and every write-back.
Read the guideProcess ERP + private AIAI for ERP in Chemicals and Batch Process Manufacturing
AI for a chemicals ERP answers questions over recipes, process orders, batch attributes, yields and regulatory data without anyone building a report first. Private, read-only deployment keeps formulas and customer specifications inside your network while planners, chemists and EHS staff get faster, evidence-backed answers.
Read the guideFSMA + ERP AIAI for Food Manufacturing ERP: FSMA Traceability and Recall Readiness
AI on a food manufacturer's ERP answers lot trace-back, allergen and supplier questions in minutes, from the lot, receiving and shipping records you already keep. It supports FSMA 204 and recall readiness by assembling evidence for people to review, while running read-only on your own infrastructure.
Read the guideCosmetics ERP + AIAI for Cosmetics and Personal Care ERP: Formulas, MoCRA and Lot Traceability
AI for a cosmetics ERP answers questions across formulas, INCI ingredient lists, batches, packaging components and complaints, so regulatory and operations teams can support MoCRA and EU requirements faster. Run privately and read-only, it keeps formulations confidential and leaves safety and labeling decisions to qualified people.
Read the guideSemiconductor ERP + AIAI for Semiconductor ERP: Wafer Lots, Yield Planning and Export Control
In semiconductor companies the ERP carries planning, subcontract flows, inventory and finance, while the fab or test floor runs on MES and equipment systems. AI over the ERP answers lot genealogy, yield-adjusted supply and export control questions from your own data, running privately so process and customer information stays inside your network.
Read the guideMachinery builders + ETO ERPAI for ERP in Industrial Machinery and Engineer-to-Order Manufacturing
AI for an industrial machinery ERP reads project costs, engineering and manufacturing BOMs, purchase orders and milestone billing together, so a project manager can ask where a machine really stands against budget and delivery. It runs on open-weight models inside your own network, because machine designs, customer specifications and quotes are the intellectual property of an ETO business.
Read the guideMetal fabrication + ERP AIAI for ERP in Metal Fabrication: Nesting, Traceability and Quoting
AI for a metal fabrication ERP connects quotes, nesting results, material certs, routings and outside processing so estimators, schedulers and quality staff can ask questions in plain language and see the query. It runs on open-weight models inside your network, so customer drawings and pricing never reach a public AI service.
Read the guideMolding + rubber processing AIAI for ERP in Plastics and Rubber Manufacturing
AI for a plastics or rubber ERP ties resin and compound lots, mold and press data, cycle times and scrap to the work order, so planners and quality staff can ask where a part's cost or defect actually came from. It runs on open-weight models inside your network and reads ERP and machine data without moving it to a public AI service.
Read the guidePackaging + print ERP AIAI for ERP in Packaging and Printing: Estimating, Waste and Job Tickets
AI for a packaging or printing ERP reads job tickets, estimates, substrate and ink usage, press schedules and tooling records, so estimators, schedulers and customer service can ask why a job cost more or is late, and see the query. It runs on open-weight models inside your network so artwork and customer pricing stay private.
Read the guideFurniture + cabinet ERP AIAI for ERP in Furniture and Woodworking Manufacturing
AI for a furniture or woodworking ERP ties configured orders, cut lists, panel and lumber yield, finishing and installation schedules to the work order, so planners and estimators can ask why a job cost more or shipped late and see the query. It runs on open-weight models inside your network, keeping designs and customer pricing private.
Read the guideEV battery manufacturing + private AIAI for the ERP Behind an EV Battery Plant: Genealogy, Yield and Passport Data
AI ERP for EV battery manufacturers puts a private language model over the ERP and the systems around it so quality, planning and compliance teams can ask questions about lot genealogy, cell grading, supplier material and scrap in plain English. It runs on the customer's own infrastructure, which matters because electrode recipes, yield curves and customer pack designs are among the most sensitive data a battery maker holds.
Read the guideSolar manufacturing + private AIAI ERP for Solar and Renewable Equipment Makers: Traceability, Binning and Warranty
AI ERP for solar and renewables manufacturing connects a private language model to the ERP so supply chain, quality and warranty teams can query cell lots, module power classes, supplier origin records and warranty claims in plain English. It is built for the traceability and customs documentation that module, inverter and tracker makers now need to produce on request.
Read the guideOil and gas equipment + private AIAI ERP for Oil and Gas Equipment Manufacturers: Heat Traceability, MTRs and Aftermarket
AI ERP for oil and gas equipment manufacturers lets quality, planning and aftermarket teams query heat numbers, material test reports, NDE and hydrotest records, and open engineered-to-order jobs through a private language model on top of the ERP. It is designed for API-style quality systems where proof of material and testing must be retrievable per serial number.
Read the guideHVAC manufacturing + private AIAI ERP for HVAC Manufacturers: Configure-to-Order, Seasonality and Warranty
AI ERP for HVAC manufacturers lets sales engineering, planning and warranty teams query configured orders, seasonal demand, refrigerant-specific bills of material and warranty history through a private language model on the ERP. It addresses configure-to-order complexity, regulation-driven product changes and commodity-exposed costs without sending product or channel data to a public model.
Read the guideRobotics OEMs + private AIAI ERP for Robotics and Automation OEMs: As-Built BOMs, Firmware and Field Service
AI ERP for robotics and automation OEMs gives engineering, supply chain and service teams a private language model over the ERP to query as-built serial configurations including firmware, long-lead component exposure, project milestones and RMA history. It is built for products where hardware, software revisions and customer-specific builds all live in different systems.
Read the guideMedical devices + QMSR + AIAI on Your ERP for Medical Device Manufacturing Under QMSR and ISO 13485
AI for a medical device ERP lets quality, regulatory and operations staff ask plain-English questions across lots, device history records, complaints, CAPA and supplier records, with each answer grounded in live data and the query shown. It runs on infrastructure you control, so design and production records stay inside your quality system boundary, and any record it drafts needs a person's approval.
Read the guideAutomotive + IATF 16949 + AIAI on Your ERP for Automotive Suppliers Working Under IATF 16949
AI for an automotive supplier ERP answers plain-English questions about EDI releases, shipments, quality escapes, SCARs and PPAP status from live data, and drafts the routine paperwork for human approval. It runs on infrastructure you control, so OEM programs, pricing and drawings stay inside your boundary.
Read the guideTextiles + apparel + AIAI on Your ERP for Textile and Apparel Manufacturing
AI for a textile or apparel ERP answers plain-English questions across style-color-size matrices, fabric lots, cut plans, time-and-action calendars and buyer orders, using live data with the query shown. It runs on infrastructure you control, so buyer tech packs, costing and order books stay private.
Read the guideBuilding products + AIAI on Your ERP for Building Products Manufacturers
AI for a building products ERP answers plain-English questions about configured orders, project quotes, material yield, delivery routes and product certifications from live data. It runs on infrastructure you control, so project pricing, dealer terms and specifications stay private, and drafts need human approval.
Read the guideContract manufacturing + AIAI on Your ERP for Contract Manufacturers and EMS Providers
AI for a contract manufacturer's ERP answers plain-English questions about quotes, consigned inventory, customer orders, NPI builds and quality across many customers, while keeping each customer's data separate. It runs on infrastructure you control, so no customer's designs or pricing reach a public model or another customer.
Read the guideAR collections + private AIAI for ERP Accounts Receivable and Collections, Grounded in Your Open Items
AI for ERP accounts receivable reads open customer items, payment history, disputes and order status from the ERP, then ranks who to call first, drafts the follow-up with the real invoice facts, and explains why DSO moved. It does not post cash or release holds on its own: a collector approves every action, and every number traces back to a query against the ledger.
Read the guideCredit management + private AIAI for ERP Credit Management: Limits, Holds and Exposure Without Spreadsheets
AI for ERP credit management pulls open orders, open invoices, payment history and external credit data into one view, then helps a credit manager review limits, triage orders on credit hold and answer exposure questions in plain English. Decisions to raise a limit or release an order stay with a person; the AI prepares the evidence and the draft.
Read the guideS&OP + private AIAI for Sales and Operations Planning, Built on the Numbers Already in Your ERP
AI for S&OP prepares the monthly cycle from ERP data: it compares forecast to actuals, flags supply and demand gaps by family, answers what-if questions in plain English, and drafts the meeting pack. It supports the planning team's judgment and does not replace a dedicated planning engine; every figure traces to a query or a stated assumption.
Read the guideMRP exceptions + private AIAI for MRP Exception Management: From 5,000 Action Messages to the 50 That Matter
AI for MRP exception management reads the action messages MRP generates (reschedule in, reschedule out, cancel, expedite), ranks them by actual customer and cost impact, and explains each with its pegging and supplier context. The planner approves and executes in the ERP; the AI prepares the worklist and the reasoning.
Read the guideSupplier onboarding + private AIAI for ERP Supplier Onboarding: Document Checks, Duplicates and Approval Packs
AI for supplier onboarding reads the forms, certificates and bank details a new vendor submits, checks them against your requirements, flags duplicates and risky changes in the ERP vendor master, and assembles the approval pack. A procurement or finance approver creates or activates the vendor record; the AI prepares the evidence.
Read the guideERP production scheduling AIAI Production Scheduling on Top of Your ERP: Explain, Triage and Re-Plan Without Replacing the Scheduler
AI for ERP production scheduling does not replace your finite-capacity scheduler. It sits beside it, reads work orders, routings, work center calendars and material status, explains why a job is late, flags data that makes the schedule wrong, and drafts reschedule options for a planner to approve. It can run on-prem against live ERP data.
Read the guideCycle counting + inventory AIAI for Cycle Counting and Inventory Accuracy: Count the Right Items and Find Why the Numbers Drift
AI improves cycle counting by choosing what to count from transaction patterns, not only ABC class, and by tracing count variances back to likely root causes such as unit of measure errors, unreported scrap, wrong location moves and receiving mistakes. It reads ERP transactions read-only and leaves count posting to your inventory team.
Read the guideERP cost variance AIAI for Cost Accounting and Variance Analysis: Explain Why Standard and Actual Cost Diverged
AI for cost accounting reads ERP standard costs, job costs and ledger postings, decomposes variances into price, usage, efficiency, rate and volume effects, and drafts commentary with the underlying transactions. It does not set standards or post journals. It shortens the investigation that month-end cost teams do by hand.
Read the guideWarranty + RMA AIAI for Warranty Claims and Returns (RMA): Check Coverage, Find Failure Patterns and Close the Loop to Quality
AI for warranty and RMA work reads ERP sales, serial, service and return records to check coverage, draft disposition recommendations, and group failures by part, lot or supplier so quality can act. A person approves credits, replacements and supplier chargebacks. It runs read-only and can stay on-prem.
Read the guideSpare parts + aftermarket AIAI for Spare Parts and Aftermarket Operations: Stock the Right Parts, Identify Them Fast and Quote Consistently
AI for aftermarket parts reads ERP item, installed base, demand and supplier data to help identify the right part from a vague request, follow supersession chains, suggest stocking levels for slow and erratic demand, and draft quotes with consistent pricing. People approve stocking and price decisions. It can run on-prem.
Read the guideBy country and regulation
Data residency, export control and privacy law shape where ERP AI can run. These guides are written for buyers in each market.
AI for SAP in the German Mittelstand: On-Prem and DSGVO-Ready
FranceAI for ERP in France: Souverainete and SecNumCloud-Aligned Architecture
United KingdomAI for ERP in UK Defence Manufacturing: On-Prem, DEFCON 658-Aware
European UnionEU AI Act Compliance for AI Built on Your ERP
European UnionBuilding GDPR-Compliant AI on Top of Your ERP
European UnionNIS2 Compliance for AI on Your Manufacturing ERP
NordicsAI for Nordic Manufacturers Running Infor M3 or IFS
ItalyAI for Italian Manufacturers on SAP, Infor, or TeamSystem
SwitzerlandAI for Swiss Manufacturers Without Sending Data Abroad
European UnionOn-Prem AI for European Defence and NATO Supply Chain Manufacturers
AustraliaAI for ERP in the Australian Defence Industry: DISP-Aligned and On-Prem
AustraliaAI for ERP in Australian Manufacturing: Practical, On-Prem or Local Cloud
JapanAI for ERP in Japanese Manufacturing: On-Prem, APPI-Aligned, and Monozukuri-Ready
CanadaAI for ERP in Canada: Controlled Goods, PIPEDA, and On-Prem Design
MexicoAI for ERP in Mexico: Nearshoring, Maquiladoras, and Bilingual Plants
Middle EastAI for ERP in the UAE and Saudi Arabia: Sovereign, On-Prem Design for Defence Manufacturing
IsraelAI for ERP in Israel: Defense Export Controls and Air-Gapped Deployment
TaiwanAI for ERP in Taiwan: Electronics, Semiconductor Supply Chain, and On-Prem AI
SingaporeAI for ERP in Singapore Aerospace MRO: On-Prem and PDPA-Ready
South KoreaAI for ERP in South Korea: On-Prem AI for Electronics and Battery Manufacturers
Start with one question your ERP cannot answer today
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