Industrial · Trade secrets · ITAR
Your process knowledge, made conversational — and kept in the building.
A tuned on-premise model turns decades of CAD notes, tolerances, failure analyses, and supplier terms into an internal expert every engineer can query — with trade secrets and ITAR-controlled technical data never leaving infrastructure you own.
The problem
The archive that can never touch a public chatbot.
A precision manufacturer’s edge is not on its website. It is in the archive: why each tolerance was set, which failure modes were chased down and how, what the supplier terms actually say, where the margin lives. That knowledge is exactly what an AI assistant would make most valuable — and exactly what can never be pasted into a public chatbot.
Trade-secret protection in Canada and the US rests on reasonable measures to maintain secrecy. Every prompt to a third-party AI describes how you work, transits infrastructure you don’t control, and lands in logs governed by someone else’s retention policy. Your processes, tooling decisions, and pricing logic are precisely the material that trains the systems that could commoditize you.
For firms in the defense supply chain, ITAR raises the stakes from commercial risk to export-control violation: technical data covered by the USML cannot flow through systems outside your authorization boundary, which rules out most public AI APIs outright.
For Canadian firms supplying US defense primes, ITAR flow-downs and Controlled Goods Program obligations impose the same discipline: controlled technical data stays on authorized systems, which rules out most public AI APIs outright.
And the knowledge problem compounds annually: every retirement takes another slice of the archive’s meaning with it. The firms that solve this are the ones that make the archive answer questions.
The architecture
An internal expert with per-project clearances.
We deploy GLM-5.2 with Qwen3-VL retrieval over your PLM and CAD archive — drawings, engineering-change orders, failure reports, supplier documents — with Kimi K3 available at the managed cluster tier for the heaviest reasoning workloads. The model is fine-tuned on your corpus, inside your environment, and evaluated against questions your senior engineers wrote.
The deployment pattern is fully air-gap-capable, with per-project access controls that mirror your program boundaries and ITAR authorization scopes. Open weights carry no telemetry and no license-check callbacks — nothing in the stack needs a route to the internet.
Deployment blueprint
The precision manufacturer, on paper.
A precision manufacturer turns its process archive into an air-gapped internal expert — searchable, conversational, and available to every engineer, with controlled technical data never leaving authorized systems. Read the full reference architecture.
Read the manufacturing blueprintQuestions we get
Frequently asked questions
Why can’t engineering teams just use cloud AI with process data?
Because process knowledge is trade-secret law’s textbook subject matter, and trade-secret protection depends on reasonable measures to maintain secrecy. Tolerances, failure analyses, supplier terms, and pricing logic pasted into a public chatbot flow through a third party’s infrastructure, logs, and retention policies — precisely the kind of disclosure that weakens a secrecy claim. For firms handling ITAR-controlled technical data, the bar is harder still: that data cannot transit systems outside your authorization boundary at all.
What does an on-premise model do with a manufacturing archive?
It turns the archive into an internal expert. A tuned model with retrieval over your PLM and CAD archive answers questions the way your most senior engineer would: why a tolerance was set, how a failure mode was resolved in 2013, which supplier terms govern a part. It is searchable, conversational, available to every engineer on every shift — and it stops institutional knowledge from walking out the door with retirements.
Can the deployment handle drawings and scanned documents, not just text?
Yes. Qwen3-VL provides document understanding and OCR over drawings, scanned engineering-change orders, forms, and handwritten notes, feeding the same retrieval layer as your text corpus. The language workhorse (GLM-5.2, or Kimi K3 at cluster scale) reasons across all of it in contexts up to one million tokens.
Does this work fully air-gapped?
Yes. Open weights are static files with no telemetry and no license-check callbacks, so the full stack — inference, retrieval, and fine-tuning — runs on a network with no route to the internet. For ITAR programs and classified-adjacent environments, air-gapped operation with per-project access controls is the default pattern, not a special case.
Go deeper
Air-gapped LLM deployment guide
CMMC/ITAR patterns — and the telemetry and license-check gotchas.
Best open-weight LLM for enterprise, 2026
DeepSeek vs Qwen vs Llama vs Kimi — comparison tables.
What on-premise LLM deployment costs
Hardware tiers, and the ~2M tokens/day break-even against APIs.
Deploy GLM-5.2 on-premise
The single-rack workhorse: 744B MoE, 40B active, MIT-licensed.
Deploy Qwen3-VL on-premise
Drawings, scans, and forms — multimodal retrieval over the archive.
Deploy Kimi K3 on-premise
Cluster-tier reasoning: 2.8T parameters, 1M-token context, vision.
Make the archive answer questions — without letting it leave.
The two-week sovereignty assessment maps your data classes, controlled-data obligations, and workloads, and hands you a written architecture with a real cost model.
Book a sovereignty assessment