Open-source LLMs for Manufacturing.
Your designs, process parameters, and maintenance know-how are the trade secrets a hosted API should never see. We deploy self-hosted open models — Llama, Mistral, Qwen — on-prem and at the edge so IP never leaves the plant, fine-tuned on technical and maintenance language so the model speaks the language of your specs, SOPs, and equipment.
Why manufacturers self-host their models
A manufacturer's edge lives in knowledge that is not public: product designs, process parameters, tooling and yield data, supplier terms, and the maintenance experience accumulated on the line. Sending any of that to a hosted LLM API means handing trade secrets to an outside provider that may retain or train on them — an unacceptable exposure when the same knowledge is what competitors lack. Open-weight models keep it inside: the model runs on-prem or at the edge, and process know-how never leaves the plant.
Self-hosting also fits the physical reality of manufacturing. Plants are often bandwidth-constrained or air-gapped, and a cloud round-trip is a poor fit for shop-floor use; a quantized open model running locally answers at the line without connectivity dependence. Tuned on your technical and maintenance language — part numbers, fault codes, procedure steps — the model reads documentation the way your engineers and technicians do, with cost fixed to hardware you own rather than a per-token meter.
Built for the plant floor.
Open models selected, adapted, and served around IP protection, edge deployment, and technical language.
- In-plant inference
- Trade secrets never leave
- No external retention or training
- LoRA / QLoRA on your corpus
- Specs, SOPs & maintenance text
- Training stays in-environment
- On-prem & edge deployment
- Quantized for local hardware
- Works in air-gapped plants
Where open-source LLMs unlock value in Manufacturing
Value concentrates wherever IP must stay in the plant, language is technical, or connectivity is constrained:
- Maintenance & troubleshooting support — technicians query manuals, fault histories, and SOPs through a model tuned on your equipment, running locally at the line.
- Engineering knowledge search — engineers retrieve specs, tolerances, and prior solutions from a private model that keeps design IP inside the company.
- Quality & process documentation — drafting and summarizing inspection reports, NCRs, and work instructions runs on owned capacity without exposing process data.
- Edge deployment in the plant — quantized models run in bandwidth-limited or air-gapped facilities, putting a capable assistant on the floor without a cloud dependency.
Common questions.
Can a self-hosted open model protect our IP and trade secrets?
Yes — that is the reason to self-host in manufacturing. We deploy Llama, Mistral, or Qwen inside your environment so designs, process parameters, supplier terms, and maintenance know-how are processed where they live and never sent to a third-party API that could retain or train on them. The competitive knowledge that makes your operation distinct stays inside the plant and the company.
Can open models run on-prem or at the edge in the plant?
Yes. We deploy on-prem in your data center and, where latency or connectivity demands it, on quantized models running at the edge near the line — so plants with limited or air-gapped connectivity still get a capable model. Inference happens locally, which keeps process data inside the facility and removes dependence on a cloud round-trip for shop-floor use.
Explore related paths.
Keep process know-how in the plant.
Bring a maintenance or engineering task and the technical documentation it runs on. In thirty minutes we will show how a self-hosted open model performs against your current API — on quality, on cost, and on IP protection — and how we would deploy it on-prem or at the edge. Response inside 24 hours.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: