# On-Prem AI for CloudSuite Industrial, Without the Multi-Tenant Cloud Move > Source: https://netray.co/erp-ai/infor-cloudsuite-industrial-ai-on-prem > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer 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. ## Kind platform ## Erp Family infor ## Erp Products - Infor CloudSuite Industrial - Infor SyteLine ## Industries - Manufacturing - Aerospace - Electronics ## Region United States ## Persona CIO ## Eyebrow CSI, kept on-prem ## Intro You run CloudSuite Industrial on-prem, and that decision was not an accident. It might be driven by data residency, by a customer flow-down clause, by ITAR or CMMC scope, or simply by a multi-tenant cloud migration that is not on this year's roadmap. Meanwhile, every vendor conversation now includes an AI pitch, and most of those pitches assume you are moving, or have already moved, to a multi-tenant cloud tenant. The good news is that the AI layer and the cloud migration are two separate decisions. CSI's data lives in a SQL Server database reachable through Mongoose IDO calls whether the instance is hosted by Infor, by a third party, or in your own data center. An AI layer built against that same interface does not care where the database physically sits, and an on-prem model server means the questions your users ask, and the data behind the answers, never leave your network. This matters more than it sounds. A generative AI feature that calls out to a public model API sends a slice of your CSI data, order details, pricing, customer names, with every query. For a defense supplier or an electronics manufacturer handling export-controlled technical data, that is a real compliance question, not a hypothetical one. Keeping inference on hardware you control removes that question rather than requiring a policy exception to answer it. This page covers what an on-prem AI layer for CSI actually looks like: the connector pattern, the hardware you need to budget for, how it compares honestly to Infor's own cloud-native AI features, and the questions to ask before choosing a path. ## Pain Points ### Vendor AI pitches assume a cloud tenant **Detail:** Most generative AI demos from ERP vendors, including Infor's own, are built around and priced around their multi-tenant cloud, which is not where your production CSI instance runs. ### No appetite for a forced migration **Detail:** A move to Infor's multi-tenant CloudSuite is a multi-year, multi-million-dollar decision on its own merits; it should not be a prerequisite for getting AI value this year. ### Data leaving the network is a real risk, not a checkbox **Detail:** Sending order, pricing, and technical data to a public model API for every AI query creates a data-handling question your security and legal teams have to answer for every new feature, not once. ### Shadow AI is already happening **Detail:** Without a sanctioned option, staff paste CSI data into consumer AI tools on their own, which is a harder problem to govern than a properly deployed private system. ### IT has to justify the hardware spend without a clear ROI story **Detail:** GPU hardware for on-prem inference is a new line item, and building the business case competes with every other capital request on the CIO's desk. ## Use Cases ### Executive and operations dashboards in plain language **Description:** Operations leaders ask questions about on-time delivery, WIP value, or backlog by product line without waiting for a BI team to build a new report. **Erp Objects:** SLOrders, SLJobs, SLItemWarehouses IDOs; ION BOD summaries **Outcome:** Reduces the number of one-off reporting requests that land on a stretched BI or IT team. ### Natural-language order and inventory queries **Description:** Sales, customer service, and planning staff get answers to order status and inventory questions without navigating CSI forms directly. **Erp Objects:** SLOrders, SLItems, SLItemWarehouses IDOs **Outcome:** Cuts the time to answer a routine customer status call from minutes of navigation to a direct answer. ### MRP and planning exception summaries **Description:** An agent groups and prioritizes Planner Workbench exceptions after each MRP run so planners start with the ones that actually affect the schedule. **Erp Objects:** Planner Workbench, SLPlannerMessages IDO **Outcome:** Shortens the review of MRP action messages, particularly useful at plants running multiple regenerations a week. ### Supplier communication drafting **Description:** Draft expedite requests, delivery confirmations, and PO acknowledgment follow-ups referencing live CSI purchase order data, for a buyer to send. **Erp Objects:** SLPurchaseOrders, SLPOLines IDOs; supplier contact records **Outcome:** Cuts manual drafting time for routine supplier correspondence, leaving buyers to focus on the exceptions that need judgment. ### Cost and margin variance explanation **Description:** When a job or item shows an unexpected cost or margin variance, an agent traces it to a specific BOM, routing, or purchase price change and summarizes the cause. **Erp Objects:** SLItemCosts, SLBOM, SLRoutings IDOs **Outcome:** Turns a variance investigation that used to take an analyst an afternoon into a documented first draft in minutes. ### Document and drawing retrieval **Description:** Engineering and shop floor staff search across attached drawings, work instructions, and CSI item or job records in one query. **Erp Objects:** Document attachment tables, SLItems, SLJobs IDOs **Outcome:** Reduces time spent locating the current revision of a drawing or work instruction, particularly for newer employees. ### Onboarding and training assistance **Description:** New planners and CSRs ask the assistant how a specific CSI process or customization works instead of interrupting a senior colleague repeatedly during ramp-up. **Erp Objects:** CSI process documentation, form and field metadata, historical transaction examples **Outcome:** Shortens new-hire ramp time on CSI-specific processes without adding to a trainer's workload. ## Architecture **Overview:** The architecture is deliberately built to look like any other supported CSI integration to Infor and to your own security team: standard IDO and ION interfaces in, an approval gate on any write, and inference kept entirely inside infrastructure you own. **Layers:** #### ERP connectors **Detail:** Mongoose IDO calls against the on-prem or hosted CSI database, with ION API and BOD messages used where the customer runs ION for other integrations already. #### Data and semantic layer **Detail:** A glossary and index built from the CSI data dictionary, your custom fields, and commonly requested report definitions, refreshed on a schedule that matches your change control process. #### Model serving **Detail:** An open-weight model served with vLLM or Ollama on GPU hardware sized to concurrent user count, whether that is a single server or a small on-prem cluster. #### Retrieval and agents **Detail:** RAG grounds answers in current CSI data and linked documents; agent actions that would write to CSI stop at a review step routed to the appropriate role. #### Governance and audit **Detail:** CSI role and site security is mirrored into the assistant's access model, and every question, retrieval, and proposed write is logged for your security and audit teams. ## Deployment Options ### Air-gapped on-prem **Fit:** CSI environments with no appetite for any inference traffic leaving the building, common in defense supply chain and export-controlled manufacturing. **Detail:** Model, retrieval index, and connector run entirely inside the plant network with no outbound dependency; model updates are applied as offline packages on a schedule you control. ### Private or sovereign cloud **Fit:** CIOs who want to keep the AI workload off the plant floor's own hardware but still fully outside Infor's multi-tenant CloudSuite. **Detail:** Inference runs in a private VPC or a sovereign cloud region under your own tenancy and access controls, connected to the on-prem CSI database over a private link rather than the public internet. ### Hybrid **Fit:** Multi-plant organizations standardizing on CSI on-prem at some sites and evaluating multi-tenant CloudSuite for others. **Detail:** A common AI layer connects to whichever CSI deployment each site runs, so the AI investment is not tied to, or blocked by, the pace of any future cloud migration decision. ## Compliance ### Keeping inference on infrastructure you control avoids sending export-controlled technical data to a third-party model API, which sidesteps a deemed-export question rather than requiring a policy exception each time a new AI feature ships. **Framework:** ITAR / EAR export control ### The AI layer sits inside the same network boundary and access controls already scoped for CUI in CSI, so it does not introduce a new system boundary to assess for your next CMMC audit. **Framework:** CMMC 2.0 / DFARS 252.204-7012 ### Many CSI shops carry contractual commitments from their own customers about where data can be processed; on-prem AI keeps those commitments intact without a carve-out negotiation. **Framework:** Customer data-handling flow-downs ### Because query and retrieval logs stay on your own systems, your existing data governance and retention policy applies directly, rather than deferring to a SaaS vendor's terms of service. **Framework:** Internal data governance policy ## Integration Notes IDO calls run through Mongoose against the CSI SQL Server database exactly as any other on-prem integration would, with no requirement to expose the database beyond the existing network boundary. Where ION is already in use for other integrations, ION API and BOD messages are reused for AI-driven writes so the write path matches your existing workflow and audit patterns rather than adding a new one. A read replica handles high-volume question-answering traffic so interactive AI queries never compete with production transaction throughput. CSI role, site, and field security groups are mirrored into the AI access model at configuration time and re-synced on a schedule, so permission changes in CSI propagate automatically. GPU sizing is based on concurrent interactive users and expected query volume, not total headcount; most single-plant CSI deployments start with a single server and scale from there. Model updates and index refreshes are packaged for offline application in air-gapped deployments, matching the patch cadence your change control process already expects from CSI itself. ## Product Fit ### SyteRay **Why:** SyteRay is purpose-built for CloudSuite Industrial and SyteLine, with connectors for IDOs and ION already built, which matters for a CIO who wants a working system in a pilot timeframe rather than a custom integration project from scratch. ### Custom build **Why:** Where a CSI environment has heavy site-specific customization or needs to combine ERP data with MES, PLM, or document systems, a custom build extends the same on-prem architecture to those additional sources. ## Engagement ### Discovery **Duration:** 2-3 weeks **Deliverables:** - Current-state review of CSI deployment, hosting, and integration landscape - Data classification pass to identify export-controlled or otherwise sensitive data in scope - Hardware sizing estimate for the target deployment option - Use case shortlist ranked by business value and technical complexity ### Pilot **Duration:** 6-8 weeks **Deliverables:** - On-prem or private-cloud model server stood up and connected to a CSI sandbox - One to two use cases live for a defined pilot user group - Security review completed against your internal standards - Pilot metrics reviewed against defined success criteria ### Production **Duration:** 4-6 weeks **Deliverables:** - Production hardware or private-cloud environment hardened and monitored - Approval workflows live for any write-back use cases - Audit logging integrated with existing SIEM or log review process - Internal runbook and support handoff ### Scale **Duration:** Ongoing **Deliverables:** - Rollout to additional CSI sites or business units - Additional use cases prioritized from the discovery backlog - Capacity review as usage and concurrent user count grow - Periodic re-alignment of the semantic layer with CSI changes ## Buyer Questions - Does this require moving our CSI instance to a multi-tenant cloud tenant, or does it work against our current hosting arrangement? - Exactly where does inference run, and can we verify that no ERP data leaves our network boundary during a query? - How is this priced: is it a per-seat cloud subscription, or a fixed cost tied to hardware and project scope that we control? - How does the solution compare, honestly, to Infor's own Coleman or GenAI capabilities inside CloudSuite? - What GPU hardware do we need to buy or lease, and what is the total cost of ownership over three years? - How does the write-approval workflow integrate with our existing change control and segregation-of-duties requirements? - What happens if we do eventually move to multi-tenant CloudSuite: does the AI investment carry over? - Who has access to the query and audit logs, and how long are they retained? ## Faq ### Can we get AI value from CSI without migrating to Infor's multi-tenant cloud? **Answer:** Yes. CSI's data is reachable through Mongoose IDO calls and ION API regardless of where the database is hosted, so a private AI layer connects the same way whether your instance runs on-prem, in a private cloud, or is hosted by a third party, with no requirement to move to multi-tenant CloudSuite first. ### How is this different from Infor's own Coleman or GenAI features? **Answer:** Infor's native AI features are built around and generally licensed for the multi-tenant CloudSuite environment. An on-prem private layer runs entirely on infrastructure you control, which matters for CSI shops that stay on-prem for data residency, export control, or contractual reasons rather than by oversight. ### What GPU hardware do we actually need? **Answer:** It depends on concurrent user count and query volume rather than total headcount; a single-plant CSI deployment with interactive question-answering often runs on one GPU server, while larger multi-site or document-heavy deployments need more. Sizing is part of the discovery phase. ### Is this only relevant for defense or export-controlled manufacturers? **Answer:** No, though it matters most there. Any CIO who wants to avoid sending order, pricing, or customer data to a public model API with every AI query, for competitive, contractual, or general data-governance reasons, benefits from keeping inference on infrastructure they control. ### How long before we see results? **Answer:** A typical pilot, covering one or two use cases for a defined group of users, runs six to eight weeks after a two to three week discovery phase, which is usually enough to validate accuracy and time savings before a production decision. ### Does this work if we have heavy CSI customization? **Answer:** Custom fields and site-specific processes are mapped explicitly during discovery. The more customized the environment, the more that mapping work matters, which is why discovery is scoped separately from the pilot build. ### What happens if we later decide to move to multi-tenant CloudSuite? **Answer:** The connector and semantic layer are rebuilt against ION API for the new environment, but the model serving infrastructure, governance patterns, and use case designs carry over, so the investment is not lost if your hosting strategy changes. ## Search terms CloudSuite Industrial AI on-prem, on-prem AI for CloudSuite Industrial, CSI AI without cloud migration, Infor CloudSuite on-prem AI, private AI for CloudSuite Industrial, SyteLine AI on-prem, CloudSuite Industrial private LLM, air-gapped AI CloudSuite Industrial, CIO guide CloudSuite Industrial AI, Infor CSI generative AI, CloudSuite Industrial copilot alternative, keep CSI on-prem with AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json