CSI, kept on-prem
On-Prem AI for CloudSuite Industrial, Without the Multi-Tenant Cloud Move
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.
- ERP
- Infor CloudSuite Industrial, Infor SyteLine
- Industries
- Manufacturing, Aerospace, Electronics
- Written for
- CIO
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.
What usually gets in the way
The problems we hear most from cio teams running Infor CloudSuite Industrial.
Vendor AI pitches assume a cloud tenant
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
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
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
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
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.
Where AI earns its place in Infor CloudSuite Industrial
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Executive and operations dashboards in plain language
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.
Touches: 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
Sales, customer service, and planning staff get answers to order status and inventory questions without navigating CSI forms directly.
Touches: 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
An agent groups and prioritizes Planner Workbench exceptions after each MRP run so planners start with the ones that actually affect the schedule.
Touches: Planner Workbench, SLPlannerMessages IDO
Outcome: Shortens the review of MRP action messages, particularly useful at plants running multiple regenerations a week.
Supplier communication drafting
Draft expedite requests, delivery confirmations, and PO acknowledgment follow-ups referencing live CSI purchase order data, for a buyer to send.
Touches: 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
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.
Touches: 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
Engineering and shop floor staff search across attached drawings, work instructions, and CSI item or job records in one query.
Touches: 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
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.
Touches: 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.
Reference architecture
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.
- 1
ERP connectors
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.
- 2
Data and semantic layer
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.
- 3
Model serving
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.
- 4
Retrieval and agents
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.
- 5
Governance and audit
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.
Integration notes for your ERP team
- 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.
Deployment options
Air-gapped on-prem
CSI environments with no appetite for any inference traffic leaving the building, common in defense supply chain and export-controlled manufacturing.
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
CIOs who want to keep the AI workload off the plant floor's own hardware but still fully outside Infor's multi-tenant CloudSuite.
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
Multi-plant organizations standardizing on CSI on-prem at some sites and evaluating multi-tenant CloudSuite for others.
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 and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR / EAR export control
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.
CMMC 2.0 / DFARS 252.204-7012
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.
Customer data-handling flow-downs
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.
Internal data governance policy
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.
Where Netray fits
SyteRay
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
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.
How an engagement runs
Phase 1 . 2-3 weeks
Discovery
- -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
Phase 2 . 6-8 weeks
Pilot
- -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
Phase 3 . 4-6 weeks
Production
- -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
Phase 4 . Ongoing
Scale
- -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
Questions to ask any vendor, including us
A short list that separates real Infor CloudSuite Industrial AI work from a chatbot demo.
- 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?
Frequently asked questions
Can we get AI value from CSI without migrating to Infor's multi-tenant cloud?
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?
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?
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?
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?
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?
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?
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.
Related guides
AI for Infor SyteLine and CloudSuite Industrial
Add grounded AI to Infor SyteLine or CloudSuite Industrial: natural-language answers, agents over IDOs and ION, on-prem or CloudSuite deployment.
SyteLine + air-gapped AIAn Air-Gapped Private LLM for SyteLine, Built for Defense Suppliers
Deploy a private LLM on an air-gapped network alongside Infor SyteLine for defense suppliers: no internet egress, ITAR and CMMC-aware architecture.
ITAR + on-prem AIITAR-Compliant AI for ERP Technical Data
How to add generative AI to your ERP without creating a deemed export under ITAR. On-prem architecture patterns an Empowered Official can sign off on.
Agents + approval gatesAI Agents for ERP, Running On-Prem
A practical guide to on-prem AI agents for ERP: what they can safely automate, where human approval belongs, and how to design the guardrails.
RAG + SQL + permissionsA Private LLM Grounded on Your ERP Data
How a private LLM answers questions on your ERP data: RAG plus text-to-SQL, role-based permissions inherited from the ERP, and where each fits.
Infor AI Buyer GuideWhat an Infor AI Consulting Partner Should Actually Deliver
A CIO's guide to Infor AI consulting: what a partner should deliver on ION API, IDOs, and Data Lake, how it relates to Coleman AI, and questions to ask.
Plan it with numbers
Air-Gapped AI Readiness Assessment
A 10-question assessment that scores how prepared your organization is to deploy and operate LLMs inside an air-gapped or classified enclave.
Free ToolSovereign AI Readiness Assessment
Score your organization across eleven dimensions of sovereign AI readiness, from data residency and model provenance to cleared personnel and air-gapped operations.
Free ToolSyteLine Implementation Cost Calculator
Estimate the full first-year cost of an Infor SyteLine implementation, including licensing, services, data migration, and internal effort.
GuideOn-Prem AI for SyteLine: Architecture, Compliance, and Footprint
How to run AI over SyteLine entirely inside your network: deployment architecture, local models, air-gap support, ITAR/CMMC implications, and realistic hardware footprint.
GuideAir-Gapped LLM Deployment Patterns That Actually Work
Air-gapped LLM deployment patterns that work: offline model transfer, update workflows, monitoring without telemetry, and CMMC-ready architectures.
GuideITAR-Compliant AI Tools for Manufacturing: Requirements and Options
ITAR-compliant AI tools for manufacturing: what export control rules mean for LLMs, which vendors qualify, and how to deploy AI without a deemed-export risk.
Talk it through with an engineer who knows Infor CloudSuite Industrial
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.