SyteLine + air-gapped AI
An Air-Gapped Private LLM for SyteLine, Built for Defense Suppliers
Short answer
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.
- ERP
- Infor SyteLine, Infor CloudSuite Industrial
- Industries
- Defense, Aerospace, Electronics
- Written for
- IT Director
As IT director for a SyteLine shop supplying defense or aerospace primes, you already manage a network boundary that most software vendors were not built to respect. CUI flows through SyteLine, ITAR-controlled technical data may sit in attached drawings and specs, and your CMMC scope depends on that boundary staying intact. A generative AI feature that phones home to a vendor's cloud model API, even one marketed as enterprise-grade, is a new egress path you did not have before.
The practical requirement is straightforward to state and harder to find vendors who actually meet it: the model has to run without any internet connection at inference time, full stop, not merely with data encrypted in transit to a cloud endpoint. That means the model weights, the retrieval index, and the SyteLine connector all live inside the air-gapped or controlled network, and updates arrive as a signed offline package you review and apply on your own patch schedule, the same way you already handle SyteLine patches and Windows updates in that environment.
This is achievable today with open-weight models in the Llama, Qwen, Mistral, or gpt-oss class, served locally with vLLM or Ollama on a GPU server sized to your query volume. The connector to SyteLine uses the same Mongoose IDO calls a normal on-prem integration would, so from a network architecture standpoint the AI layer looks like just another internal application talking to the ERP database, not a new class of system your security team has to reason about from scratch.
This page walks through what an air-gapped SyteLine AI deployment actually involves: the architecture, how it maps to ITAR, CMMC 2.0, and DFARS 252.204-7012 requirements, realistic use cases for a defense manufacturing floor, and the questions to put to any vendor claiming to offer an air-gapped option, because the term gets used loosely.
What usually gets in the way
The problems we hear most from it director teams running Infor SyteLine.
Most vendor AI features assume internet access
The generative AI features showing up in ERP vendor demos, including Infor's own, are almost universally built around a cloud model API, which is a non-starter for a network segmented for ITAR or CMMC purposes.
"Cloud AI with encryption" is not the same as air-gapped
Some vendors describe encrypted transit to a cloud model as secure enough; for a network with no permitted egress, encryption in transit does not solve the actual requirement, which is no transit at all.
Shadow AI risk is higher, not lower, in restricted environments
Staff frustrated by a lack of sanctioned tools sometimes work around network restrictions to use a consumer AI tool on a personal device, which is a harder problem to detect and govern than a properly deployed internal system.
SyteLine's IDO knowledge is scarce on a defense-focused team
Small defense supplier IT teams often carry one or two people who deeply understand SyteLine's IDO structure, and any new question or report request bottlenecks on their availability.
Audit and traceability expectations are higher
A CMMC or DCMA review expects clear evidence of who accessed what CUI and when; any new system touching SyteLine data has to meet that bar from day one, not grow into it later.
Where AI earns its place in Infor SyteLine
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Order and job status queries with no data leaving the network
Program managers and CSRs ask about order, job, and shipment status in plain language, with every part of the query and response staying inside the controlled network.
Touches: SLOrders, SLJobs, SLJobRoutes IDOs
Outcome: Gives program-facing staff fast status answers without adding a new egress path to the network boundary.
MRP exception triage for a lean planning team
An agent groups and prioritizes MRP action messages after each regeneration, which matters most for defense suppliers running small planning teams with no slack for manual triage.
Touches: Planner Workbench, SLPlannerMessages IDO
Outcome: Reduces the planner time spent scanning action messages, freeing capacity for the exceptions that actually need judgment.
Purchase order expediting for critical program parts
Drafts expedite communications for late purchase order lines on schedule-critical parts, referencing live SyteLine PO data, for a buyer to review.
Touches: SLPurchaseOrders, SLPOLines IDOs
Outcome: Cuts the manual drafting time buyers spend chasing suppliers on parts tied to program delivery dates.
First article inspection and quality record drafting
Drafts a first pass at first article inspection reports (AS9102-aligned) or non-conformance records from inspection data, for quality staff to finalize.
Touches: Quality module tables, SLInspections IDOs
Outcome: Gives quality staff a structured draft instead of a blank AS9102 form, shortening documentation turnaround.
Engineering document and specification search
Engineers and shop floor staff search across attached drawings, specifications, and prior non-conformance records tied to a specific item or job, all inside the network boundary.
Touches: Document attachment tables, SLItems, SLJobs IDOs
Outcome: Reduces time spent locating the correct drawing revision or spec, particularly relevant where a wrong revision has real consequences.
Cost rollup and variance explanation for program cost tracking
Traces a cost or margin variance on a program-related item back to a specific routing, BOM, or purchased-cost change.
Touches: SLItemCosts, SLRoutings, SLBOM IDOs
Outcome: Speeds up variance investigation that feeds program cost reporting to a prime or contracting officer.
Onboarding assistance for cleared staff without external references
New planners and buyers ask the assistant how a specific SyteLine process or customization works, useful in an environment where staff cannot simply search the open internet for help.
Touches: SyteLine process documentation, form and field metadata
Outcome: Shortens ramp time for new staff on SyteLine-specific processes without relying on external resources unavailable inside the network.
Reference architecture
Every component of the deployment is designed to run with no outbound internet dependency at inference time, and every update path is an offline, reviewable package rather than a live connection to a vendor service.
- 1
ERP connectors
Mongoose IDO calls against the on-prem SyteLine SQL Server database, read-only by default, running entirely inside the controlled network with no external routing.
- 2
Data and semantic layer
A business glossary and retrieval index built from the SyteLine data dictionary and attached engineering and quality documents, indexed and stored entirely on local infrastructure.
- 3
Model serving
An open-weight model (Llama, Qwen, Mistral, or gpt-oss class) served with vLLM or Ollama on a GPU server inside the air-gapped network, with no license-check or telemetry call to an external endpoint.
- 4
Retrieval and agents
RAG grounds answers in current SyteLine data and local documents; any agent-drafted write, a PO expedite, a quality record, stops at an approval step before it touches SyteLine.
- 5
Governance and audit
SyteLine role and site security is mirrored into the assistant's access model, and every query, retrieval, and proposed write is logged locally in a form suitable for a CMMC or DCMA audit.
Integration notes for your ERP team
- Mongoose IDO calls against the on-prem SyteLine database run entirely within the controlled network, with no requirement to expose the database to any external endpoint.
- Model weights and application software are transferred via offline media (approved removable media or a controlled one-way transfer process) rather than a live download, matching how many air-gapped environments already handle software updates.
- SyteLine role, site, and field security groups are mirrored into the AI access model at deployment and re-synced on a schedule you control.
- GPU sizing is based on the number of concurrent users and expected query volume for a single-plant defense supplier, typically smaller than a multi-site commercial deployment.
- Attached engineering drawings and quality documents are indexed locally, with the index itself subject to the same access controls as the source documents.
- Audit logs are written to local storage in a format compatible with your existing log review and retention process, not a vendor-hosted logging service.
Deployment options
Fully air-gapped on-prem
SyteLine shops on a network with no permitted internet egress at all, the default expectation for many ITAR-scoped defense supplier environments.
Model weights, retrieval index, and SyteLine connector are installed via offline media transfer and run with zero outbound network dependency; all updates arrive as signed packages reviewed before installation.
Controlled network with monitored egress
Suppliers whose network allows tightly monitored, logged egress for specific approved purposes but still wants AI inference to stay internal.
Inference stays fully local while model updates are pulled through the existing monitored update channel already used for other approved software, rather than requiring physical media for every patch.
CMMC enclave-scoped deployment
Organizations with a defined CMMC assessment boundary or CUI enclave that SyteLine already sits inside.
The AI layer is deployed entirely within the existing enclave boundary, inheriting its access controls and monitoring rather than expanding the scope your assessor has to review.
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
Because inference runs with no internet connection at all, there is no transmission of ITAR-controlled technical data to any external party, removing the deemed-export question that a cloud-connected AI feature would raise.
CMMC 2.0 Level 2
The deployment is scoped inside your existing CUI boundary, so it does not expand your assessment scope; access controls, logging, and configuration management follow the same NIST SP 800-171 practices already applied to SyteLine.
DFARS 252.204-7012
Incident reporting and safeguarding requirements for covered defense information extend naturally to the AI layer because it operates inside the same protected environment SyteLine already runs in, rather than as a separate system outside that coverage.
AS9100 / quality traceability
Quality record drafting use cases are built to produce documentation that supports, rather than replaces, your existing AS9100 quality management system and its audit trail requirements.
DCMA / customer audit readiness
Local audit logging of every query and write action gives you a clear answer when a DCMA representative or prime contractor auditor asks what the AI layer can access and what it has done.
Where Netray fits
SyteRay
SyteRay's SyteLine-specific connector work, built for IDOs and Mongoose, is the direct fit for a defense supplier that needs a working air-gapped deployment without a lengthy custom integration build.
Custom build
Where the environment has a specific CMMC enclave architecture, custom document classification requirements, or non-standard SyteLine customizations, a custom build tailors the deployment to those specifics.
How an engagement runs
Phase 1 . 2-3 weeks
Discovery
- -Network architecture and CUI/ITAR boundary review
- -Inventory of SyteLine IDOs and attached document sources in scope
- -Offline update process agreed with your security team
- -Use case shortlist prioritized for defense program value
Phase 2 . 6-8 weeks
Pilot
- -Air-gapped model server installed and connected to a SyteLine test environment
- -One to two use cases live for a defined program team
- -Access control mirrored to SyteLine roles and sites
- -Pilot results reviewed against agreed success criteria
Phase 3 . 4-6 weeks
Production
- -Production deployment inside the CUI enclave, hardened and monitored
- -Write-approval workflows configured for PO and quality record use cases
- -Local audit logging validated against your CMMC or DCMA audit expectations
- -Internal runbook and training for the IT and program teams
Phase 4 . Ongoing
Scale
- -Rollout to additional programs or business units within the enclave
- -Additional use cases added from the discovery backlog
- -Scheduled offline update reviews and package application
- -Capacity review as query volume grows
Questions to ask any vendor, including us
A short list that separates real Infor SyteLine AI work from a chatbot demo.
- Can you demonstrate the model running with the network cable physically disconnected, not just with encrypted transit to a cloud endpoint?
- How are model and software updates delivered, and does that process fit inside our existing offline patch management procedure?
- Does the deployment expand our current CMMC assessment scope, or does it stay entirely inside our existing CUI boundary?
- What does the local audit log capture, and has it been reviewed against what a DCMA or prime contractor audit would expect to see?
- How does the assistant respect SyteLine's existing role and site security rather than creating a separate access model to manage?
- What GPU hardware do we need, and can it be procured and installed within our existing controlled-network procurement process?
- Who has access to the model weights and configuration, and is there any dependency on a vendor connection after deployment?
- What happens if we need support and the vendor cannot remotely connect to our network?
Frequently asked questions
Is a truly air-gapped AI deployment for SyteLine actually possible?
Yes. Open-weight models like Llama, Qwen, Mistral, or gpt-oss class models can be served entirely on local GPU hardware with vLLM or Ollama, with no internet connection required at inference time. The SyteLine connector uses standard Mongoose IDO calls against the local database, so the whole stack runs with zero outbound dependency once deployed.
How are model updates handled if there is no internet access?
Updated model weights and software packages are transferred via approved offline media or a controlled one-way transfer process, reviewed by your security team before installation, the same pattern many air-gapped environments already use for other software updates.
Does this expand our CMMC assessment scope?
It should not, if deployed correctly. The AI layer is installed inside your existing CUI enclave boundary and inherits its access controls, network segmentation, and monitoring, rather than introducing a new system your assessor has to evaluate separately.
Does this avoid ITAR deemed-export concerns?
Because inference happens entirely on infrastructure inside your controlled network with no data transmitted to any external party, there is no transmission of ITAR-controlled technical data outside the United States or to a foreign person, which is the deemed-export scenario a cloud-connected AI feature would risk.
What hardware do we need to budget for?
Sizing depends on concurrent users and query volume; most single-plant defense supplier deployments run on a single GPU server. This is scoped precisely during the discovery phase against your actual user count and use cases rather than a generic estimate.
How is this different from a regular SyteLine AI integration?
The connector pattern to SyteLine is the same. What differs is the deployment: no internet dependency at any point, offline update packages instead of live vendor connections, and local audit logging built to satisfy CMMC and DCMA-style review rather than a standard commercial audit requirement.
Can support staff troubleshoot the system remotely?
Only through whatever controlled, monitored access process your network already permits for other vendors, if any. Many air-gapped deployments are designed so your own internal IT staff, trained during the engagement, can run day-to-day operations and troubleshooting without any vendor connection at all.
Related guides
ITAR-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.
CMMC 2.0 + on-prem AICMMC Level 2 AI for ERP Without Blowing Up Your Scope
How to deploy AI inside your CMMC 2.0 Level 2 assessment boundary without expanding CUI scope. Enclave architecture a CISO can defend to a C3PAO.
Infor SyteLine / CSI + AIAI 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.
On-prem AI, any ERP, A&DOn-Prem AI for ERP in Aerospace, Defense, and Electronics Manufacturing
A hub guide to on-prem AI across SAP, Infor LN, Costpoint, IFS, and Oracle EBS for aerospace, defense, and electronics manufacturers under ITAR, CMMC, and AS9100.
DFARS 7012 + NIST 800-171Mapping DFARS 7012 and NIST 800-171 Controls to AI on Your ERP
Map DFARS 252.204-7012 and NIST SP 800-171 control families to an AI system layered on your ERP, with evidence a Compliance Manager can defend.
AS9100D + on-prem AIAI for AS9100 Quality Management on Your ERP
AI on top of your ERP quality module for AS9100D suppliers: NCR/CAPA drafting, FAI support, counterfeit parts screening, with a full audit trail.
Plan it with numbers
ITAR AI Workload Compliance Assessment
Score your AI deployments across eight dimensions of ITAR exposure, from technical data classification and US persons access control to technology control plan coverage.
Free ToolAir-Gapped LLM Deployment Checklist
A practical control checklist for deploying and maintaining large language models in a fully air-gapped environment, from initial staging through ongoing patching and drift detection.
Free ToolDefense Contractor AI Readiness Assessment
A 9-question assessment measuring whether your defense manufacturing business can adopt AI productively and compliantly - across governance, data, infrastructure, and skills.
GuideOn-Prem AI for Defense Contractors: The Complete Guide
On-prem AI for defense contractors: deploy LLMs and AI agents inside your CMMC and ITAR boundary. Architecture, hardware costs, timelines, and vendor options.
GuideITAR and CMMC Handling of AI Workloads
How ITAR and CMMC apply to AI workloads: technical data boundaries, CUI handling, assessed environments, and where on-prem AI is the only option.
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.
Talk it through with an engineer who knows Infor SyteLine
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.