# AI for Genius ERP, Grounded in Your Engineering and Job Cost Data > Source: https://netray.co/erp-ai/genius-erp-ai-integration > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer 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. ## Kind platform ## Erp Family other ## Erp Products - Genius ERP - Genius ERP Cloud ## Industries - Custom Manufacturing - Metal Fabrication - Machine Building - Job Shop / Make-to-Order ## Region Global ## Persona Owner ## Eyebrow Genius ERP + private AI ## Intro Genius ERP earned its place with custom manufacturers, metal fabricators, and machine builders because it was built specifically for engineer-to-order and configure-to-order work: the Engineering module handles a product configurator and BOM/routing generation per job, and the Production module runs a finite capacity schedule against real work centers instead of a generic planning board bolted onto a distribution system. That specialization is exactly why a small operations team runs the shop on it without a dedicated ERP administrator. The cost of that lean setup shows up at month end. Knowing which jobs actually made money after labor, scrap, and engineering change orders means pulling job cost transactions and reconciling them against the original quote, usually by whoever has an hour free between running the floor and approving purchase orders. Quoting new work leans on memory of what a similar job cost last time rather than a systematic pull from Genius ERP's own history. A private AI layer grounded on your Genius ERP data changes that math. An owner can ask 'why did job 2214 run over on labor' and get an answer built from the actual routing steps, labor entries, and engineering change history for that job, with the underlying records shown so it can be checked rather than trusted blindly. The same grounding supports a quoting assistant that pulls real historical costs for similar configurations when a new RFQ lands. Because Genius ERP customers are split between on-premise SQL Server installs and Genius ERP Cloud, the AI layer's data placement question has a clean answer either way: for an on-premise shop, the model and extract can sit on the same network as the ERP; for Genius ERP Cloud customers, the extract and model run in a private environment the owner controls, so job cost and customer data are never processed by a public model API regardless of hosting mode. ## Pain Points ### Job profitability is reconstructed after the fact **Detail:** Knowing whether a job made money after labor, scrap, and engineering changes usually happens weeks after close, once someone has time to pull and reconcile the cost transactions by hand. ### Quoting relies on memory, not history **Detail:** Estimators price new custom work based on what they remember from similar past jobs rather than a systematic look at actual costs recorded against comparable configurations. ### Engineering changes ripple quietly through open jobs **Detail:** An engineering change order can affect BOM cost, routing time, and material availability on an open job, but tracing that ripple effect takes a person who knows the Engineering module well. ### Scheduling questions interrupt the scheduler constantly **Detail:** Sales, purchasing, and the shop floor all want a status answer, and each one becomes an interruption to the person who actually understands the finite capacity schedule. ### No dedicated analyst to build custom reports **Detail:** Lean shops running Genius ERP rarely have a BI resource, so most ad hoc questions get answered from a spreadsheet export rather than a fresh query against current data. ## Use Cases ### Job cost and margin explanation **Description:** An owner or estimator asks why a job ran over budget and the assistant pulls labor, material, and overhead transactions against the original estimate to explain the variance. **Erp Objects:** Genius ERP Job Cost transactions, Estimates, Engineering BOM/Routing records **Outcome:** Turns a manual cost reconciliation into a same-day answer instead of a month-end surprise. ### Quoting assistant using historical job costs **Description:** When a new RFQ comes in, the assistant retrieves actual costs from similar prior configurations, by product family and material, to suggest a starting quote for the estimator to adjust. **Erp Objects:** Genius ERP Sales Quotes, historical Job Costs, Product Configurator records **Outcome:** Gives estimators a data-grounded starting point instead of relying on memory for custom, low-volume work. ### Engineering change impact summary **Description:** When an engineering change order is issued, the assistant identifies which open jobs, BOMs, and purchase orders it touches and drafts a plain-language impact summary. **Erp Objects:** Genius ERP Engineering Change Orders, BOM revisions, open Production Orders **Outcome:** Surfaces downstream impact before it causes a missed material or a scheduling conflict. ### Natural-language production status queries **Description:** Sales or the shop floor asks 'what is the status of job 2214' or 'what is behind schedule this week' and gets an answer grounded in the current finite schedule and routing status. **Erp Objects:** Genius ERP Production Schedule, Routing operations, Work Center status **Outcome:** Cuts interruptions to the scheduler for status questions the data already answers. ### Scrap and rework pattern detection **Description:** The assistant periodically reviews scrap and rework transactions across jobs and flags recurring patterns tied to a part number or work center for a supervisor to investigate. **Erp Objects:** Genius ERP Shop Floor Transactions, Scrap entries, Work Centers **Outcome:** Catches a creeping scrap problem after a handful of occurrences instead of after a large variance. ### AP and PO matching assistant **Description:** The assistant compares purchase orders, receipts, and vendor invoices and flags price or quantity mismatches for the accounting clerk to resolve. **Erp Objects:** Genius ERP Purchase Orders, Receipts, AP transactions **Outcome:** Speeds up routine three-way match review, leaving genuine exceptions for a person to handle. ### Capacity and lead-time Q&A for sales **Description:** A salesperson asks whether a promised delivery date is realistic and the assistant checks current work center load and open job backlog before answering. **Erp Objects:** Genius ERP Finite Capacity Schedule, Work Center calendars, open Job backlog **Outcome:** Reduces overpromised delivery dates that later force expediting or customer apologies. ## Architecture **Overview:** The architecture connects to Genius ERP through its SQL Server database views and API layer, keeping the model and any extracted data on infrastructure matched to how the customer already runs Genius ERP, on-premise or Genius ERP Cloud. **Layers:** #### Genius ERP connectors **Detail:** SQL Server reporting views and the Genius ERP API/OData-style layer for structured extracts of job cost, engineering, and schedule data, and for any approved write-back. #### Data and semantic layer **Detail:** Extracted job, BOM, routing, and cost data organized into a semantic model mapping Genius ERP's Engineering and Production structures to the terms an owner and estimator actually use. #### Model serving **Detail:** An open-weight model served with vLLM or Ollama on infrastructure sized to a single-plant custom manufacturer, from a small on-prem server to a private cloud tenant. #### Retrieval and agents **Detail:** Job cost, quoting, and status questions are answered through retrieval grounded in the Genius ERP extract; any write-back, such as a draft quote, goes through the API for a person to approve. #### Governance and audit **Detail:** Access mirrors Genius ERP user permissions, and every question and generated answer is logged, which matters once the assistant is used for quoting or cost decisions. ## Deployment Options ### Air-gapped or on-site, beside on-premise Genius ERP **Fit:** Shops running Genius ERP on their own SQL Server for control or customer confidentiality reasons **Detail:** The AI model and data extract run on the same on-premise network as Genius ERP, with no dependency on an outside connection for either system. ### Private cloud, matched to hosting preference **Fit:** Growing shops that want AI without standing up local GPU hardware **Detail:** The extract and model run in a private cloud VPC the owner controls, isolated from any shared inference endpoint, fed by scheduled or near-real-time pulls from Genius ERP. ### Customer-controlled layer beside Genius ERP Cloud **Fit:** Customers running the hosted Genius ERP Cloud edition **Detail:** Data is extracted via the API into a small private environment the owner controls, so job cost and customer data are never sent to a public model API even though Genius ERP itself is cloud-hosted. ## Compliance ### Job records referencing export-controlled drawings or specifications stay inside the customer's private inference environment, never passed to a public model API. **Framework:** ITAR (for defense-adjacent metal fabricators and machine builders) ### Shops running Genius ERP that also hold DoD subcontracts keep controlled unclassified information referenced in job files inside their own private AI environment, isolated from shared services. **Framework:** CMMC 2.0 / DFARS 252.204-7012 ### Customer and supplier data extracted for AI grounding is minimized to what each use case needs and stored on infrastructure the owner controls, reducing exposure versus a third-party export. **Framework:** State data breach notification laws ### The AI layer is additive to the quality record, not a replacement: every suggested job cost or engineering impact answer links back to the source Genius ERP records for auditability. **Framework:** ISO 9001 (where the shop is certified) ## Integration Notes SQL Server reporting views are the most direct path to job cost, BOM, and routing data; the semantic layer should be built against a stable set of views rather than raw production tables to survive Genius ERP updates. The Genius ERP API and OData-style feed handle structured, authenticated access for near-real-time lookups and any approved write-back, without requiring direct database credentials for every integration. Engineering change order history is relational across BOM revisions and affected jobs; that join path needs to be encoded once in the semantic layer so impact questions resolve consistently. Genius ERP Cloud customers should plan extract cadence around what each use case actually needs: job cost and quoting history tolerate a daily refresh, while scheduling status benefits from closer to real time. User permissions in Genius ERP should be mirrored into the AI layer's access model so an estimator or floor supervisor only sees answers grounded in data their own login could already reach. Any write-back, such as a draft quote or a flagged engineering impact note, should go through the Genius ERP API with a named person approving it rather than a direct database write. ## Product Fit ### ERPray **Why:** The grounded question-answering pattern fits an owner's day-to-day job cost and schedule questions well; a Genius ERP connector is scoped as part of the engagement since it is not a shipped connector today. ### Custom build **Why:** Quoting assistants and engineering change impact summaries are typically bespoke, tuned to the shop's own configurator logic and part families rather than a generic template. ## Engagement ### Discovery **Duration:** 2 weeks **Deliverables:** - Review of the Genius ERP deployment mode (on-premise or Cloud) and current reporting views - Priority use case selection with the owner and key staff - Draft semantic model for job cost, engineering, and schedule data - Data placement decision matched to how Genius ERP is hosted ### Pilot **Duration:** 5-7 weeks **Deliverables:** - SQL Server / API integration for the selected data set - Private model deployed in the agreed environment - One or two use cases live, e.g. job cost explanation and quoting assistance - Access controls mapped to existing Genius ERP permissions ### Production **Duration:** 6-8 weeks **Deliverables:** - Expansion to engineering change impact and scrap pattern detection - Query and access logging reviewed - Training for estimators, supervisors, and office staff - Handover runbook for ongoing operation ### Scale **Duration:** Ongoing **Deliverables:** - Additional use cases added from recurring assistant questions - Periodic review of quoting accuracy against actual job outcomes - Model and prompt updates as configurator options or processes change - Right-sizing of infrastructure as usage grows ## Buyer Questions - Where exactly will our job cost and customer data be stored and processed for AI, given our Genius ERP hosting mode? - Can the assistant show the underlying Genius ERP records behind a job cost or quoting answer, so we can verify it? - How does the tool's access control map to our existing Genius ERP user permissions? - Does any AI-suggested quote or engineering impact note require a named person's approval before it is acted on? - How is the connector kept working as Genius ERP ships new versions? - What happens to our extracted data and semantic model if we end the engagement? - What is the realistic ongoing infrastructure cost for a shop our size? ## Faq ### Can AI be added to Genius ERP without sending job and customer data to a public AI provider? **Answer:** Yes. Genius ERP's SQL Server views and API support extracting job cost, engineering, and schedule data into infrastructure the owner controls, where a privately hosted model answers questions grounded in that data. No job or customer data needs to reach a public model API for this to work. ### Does this work with Genius ERP Cloud as well as on-premise installs? **Answer:** Yes. For Genius ERP Cloud customers, the AI layer extracts data via the API into a private environment the owner controls, keeping the model and its data separate from both Genius ERP's hosting infrastructure and from any public model provider. ### Can the assistant draft quotes automatically for new custom work? **Answer:** It can draft a starting quote grounded in historical job costs for similar configurations, which an estimator reviews and adjusts. The recommended pattern is that the assistant proposes and a person approves before anything reaches a customer. ### How does this help with engineering change orders? **Answer:** The assistant traces an engineering change order through the BOM revisions and open jobs it affects and drafts a plain-language impact summary, which the engineer or estimator reviews. It does not apply the change itself. ### Will this replace our existing Genius ERP reports? **Answer:** No, it complements them. Standard reports remain the right tool for recurring, fixed-format output. The AI layer is better suited to ad hoc questions and drafting work that would otherwise take a person building a one-off query. ### Is this a Genius ERP product? **Answer:** No. This is a separate, privately hosted AI layer built using Genius ERP's existing database views and API, independent of Genius Solutions' own product roadmap. It gives an owner AI grounded on their own data today, on infrastructure they control. ### How long does a Genius ERP AI pilot take? **Answer:** A focused pilot on one or two use cases, such as job cost explanation and a quoting assistant, typically takes five to seven weeks from kickoff to a working assistant a handful of estimators and supervisors are using. ## Search terms genius erp ai, genius erp ai integration, genius erp generative ai, genius erp copilot, on-prem ai for genius erp, genius erp ai agent, genius erp cloud ai, genius erp custom manufacturing ai, ai for genius erp, private ai for genius erp, genius erp job costing ai, genius erp quoting ai --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json