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.
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.
Open models selected, adapted, and served around IP protection, edge deployment, and technical language.
Value concentrates wherever IP must stay in the plant, language is technical, or connectivity is constrained:
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.
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.
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.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: