For Manufacturers & Utilities
ITAR/EAR export controls, DTSA trade secret protection, and NERC CIP don't stop because your engineers want to work faster. We build private AI that knows your plant without the regulatory exposure.
What's at Stake
Manufacturing and utility AI exposure sits at the intersection of export controls, trade secret law, and critical infrastructure security. These aren't hypothetical risks.
If your manufacturing processes, technical data, or specifications involve defense articles or dual-use technologies, they are controlled under ITAR or EAR. Uploading controlled technical data to a public AI service — which routes through U.S. or foreign servers — may constitute an unauthorized export. DDTC enforcement actions for inadvertent ITAR violations run $1M+ per incident.
Federal trade secret protection under the DTSA requires "reasonable measures" to keep trade secrets confidential. Pasting proprietary formulations, process specs, or production data into ChatGPT almost certainly fails the reasonable measures test. Courts are beginning to address whether AI disclosures constitute trade secret misappropriation by the employee — and forfeiture of protection by the company.
NERC Critical Infrastructure Protection standards require utilities to protect bulk electric system (BES) cyber system information. Operational technology documentation, SCADA configurations, and network diagrams are covered. Using public AI tools to query, summarize, or analyze BES documentation may create CIP compliance violations — and the penalties are per-day, not per-incident.
Most manufacturer NDAs with suppliers prohibit sharing confidential technical data with third parties. AI tools are third parties. If your engineers are querying supplier specifications or pricing in a public LLM, you may be in breach of your NDA obligations — with exposure that doesn't require a regulatory action to materialize.
The Problem
Your supplier negotiations, proprietary process specs, and formulation data are in your documents. A public LLM trained on that content means your competitors benefit too.
Maintenance procedures, equipment configs, and SCADA change logs contain information a malicious actor would pay for. That data going to an external API is a security failure, not just a data governance issue.
When Samsung engineers pasted chip schematics into ChatGPT, it became a headline. That wasn't malicious — it was people trying to work faster. Your engineers are doing the same thing right now.
What It Does
Give technicians a private AI that knows every equipment manual, maintenance log, and SOP you have. "What's the torque spec for the bearing on line 3?" gets answered in seconds, correctly.
Query your incident reports, near-miss logs, and safety committee minutes in natural language. Surface patterns across years of data — not just the keyword matches from a SharePoint search.
Consolidate specs, quotes, and contracts from 40 different suppliers into a coherent comparison. A model trained on your vendor docs gives you a Q&A interface against your entire supplier knowledge base.
How We'd Approach It
Four phases. Fixed-price. IP and export control review built in. See the full methodology →
We inventory your technical documentation, production data, and supplier materials. We flag ITAR/EAR-controlled content, identify trade secrets requiring DTSA protection, and classify SCADA/OT documentation for utilities under NERC CIP. No data enters the training pipeline without classification sign-off.
Private AI infrastructure inside your network — your cloud VPC or on-premise servers. No external API calls, no data leaving your perimeter. For utilities, we design to NERC CIP requirements from the start. RBAC separates production floor access from engineering, from management.
Fine-tuning on your equipment manuals, SOPs, incident logs, and supplier documents. We validate against your most common technician queries and engineering research tasks. Plant managers and safety officers review outputs before go-live. No production disruption during deployment.
Model weights and deployment config transfer to you. Monthly retainer keeps the model current as equipment changes, new vendors come on board, and SOPs update. You own the IP — we maintain the system.
Sample Work Product
See the depth of a Vermont AI Systems engagement — a complete AI Readiness Assessment in Manufacturing & Utilities format.
Read the Vermont manufacturer sample case study →Interactive Demo
See what private AI looks like for a 320-employee aerospace precision manufacturer. Ask about ITAR program status, supplier NDA compliance, scrap rates, and tribal knowledge — all sourced from fictional internal records.
Try the Precision Components Vermont demo →Common Questions
We classify all documents before ingestion. ITAR/EAR-controlled content is flagged during the audit phase, and we scope the training corpus to exclude export-controlled data unless you have explicit authorization for the deployment architecture. For defense manufacturers, we've built architectures that satisfy the ITAR "U.S. Person" access requirement through infrastructure design — not just policy.
A private deployment is a prerequisite for CIP compliance — it's not sufficient on its own. We design the infrastructure to meet BES Cyber System Information (BCSI) handling requirements: access controls, audit logging, physical security for on-premise deployments, and encrypted communications for cloud VPC. We produce the CIP documentation package as part of the engagement. You still need your CIP program; we make the AI component of it defensible.
Supplier documents are classified during the audit phase. We help you identify which documents contain NDA-protected information and scope the training corpus accordingly. Where vendor data is included, we structure the deployment so the information stays inside your environment — the model learns from it, but it's never transmitted to a third party. That's a fundamentally different risk posture than querying a public LLM with supplier specs.
More questions? See the 15 questions to ask any AI vendor →
The discovery call is 30 minutes. We'll tell you exactly what it would take to build this for your organization, what it would cost, and whether we're the right fit.