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
15 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 + AIAI for Infor M3: 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 guideAI for SAP
11 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 guideAI for Oracle
13 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 guideAI for Microsoft Dynamics
8 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 guideAI for Epicor
7 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 guideAI for IFS
2 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 guideAI for Deltek
2 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 guideAI for Specialist ERPs
48 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 guideAcross every ERP
30 guidesUse cases, 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 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
We will map the data path, the model and where it runs, inside your boundary.