MANUFACTURING · OT

Manufacturing AI: Process Knowledge That Never Leaves the Plant

Your process parameters are the competitive advantage. They should not be sitting in someone else's inference log.

Written by Jakub Rusinowski · Last updated 2026-08-01 · Hardware figures computed by our VRAM engine

Manufacturing AI deployment puts the model inside the plant, on the IT side of a properly segmented network, where it can reach maintenance histories, quality records, and standard operating procedures without any of that leaving the site. Two constraints drive the architecture: process knowledge is trade secret material whose value depends on never being disclosed, and anything touching operational technology has to respect the zone-and-conduit segmentation that keeps the line safe. Neither is an obstacle — both are reasons the deployment looks the way it does.

Why this is hard

What the deployment gives you

Institutional knowledge, made searchable

Retrieval over maintenance histories, deviation reports, shift handovers, and equipment manuals, so a technician on nights gets the answer a thirty-year veteran would have given. This is the use case that reliably pays for the hardware.

Correctly placed in the zone model

Inference sits on the IT side, reading from the historian and maintenance system through defined conduits. The model informs people; it does not reach into control systems, and the segmentation that keeps the line safe stays intact.

Trade secrets stay demonstrably protected

Process parameters, formulations, and tooling specifications never leave the site. Keeping the data inside the plant is itself part of the reasonable-measures argument that trade secret protection depends on.

SOP and quality documentation support

Draft work instructions, summarise deviations, and answer questions grounded in the controlled document set — with the source revision cited, so a quality system that depends on document control is not undermined by an unsourced answer.

Where inference belongs in a segmented plant

The zone model already in place is the design. Inference lives in the IT zone with read access through defined conduits to the systems that hold the data; it never sits on the control network and never writes to it. The value comes from reading operational history, not from touching operations.

Where inference belongs in a segmented plantPlant network — IT zone, no external egress. 1. Historian & CMMS: Process history, maintenance records, and deviation reports — read-only source. 2. Document index: Controlled SOPs, manuals, and shift logs, versioned to the revision in force. 3. Inference server: Open-weight model on plant GPUs, IT zone, no route to the control network. 4. Technician interface: Answers with cited source and revision. Advisory only — never a control action.PLANT NETWORK — IT ZONE, NO EXTERNAL EGRESSHistorian & CMMSProcess history,maintenance records, anddeviation reports —read-only source.Document indexControlled SOPs, manuals,and shift logs, versionedto the revision in force.Inference serverOpen-weight model on plantGPUs, IT zone, no route tothe control network.Technician interfaceAnswers with cited sourceand revision. Advisory only— never a control action.

What you have to satisfy

Models that fit this deployment

模型VRAM (Q4)可运行于上下文许可
Qwen 3 14B
Single-plant tier — 12 GB card — Maintenance and SOP retrieval for one site on modest hardware in the plant IT rack.
ollama pull qwen3:14b		9.7 GB12 GB GPU (RTX 3060 12GB / 4070)
Mac: 16 GB 统一内存		125KApache 2.0
Qwen 3 32B
Site tier — 24 GB card — The practical default: grounded Q&A over historian data, manuals, and shift logs.
ollama pull qwen3:32b		20.6 GB24 GB GPU (RTX 3090/4090)
Mac: 32 GB 统一内存		125KApache 2.0
Llama 3.3 70B Instruct
Multi-site tier — 48 GB class — Where a central deployment serves several plants and document sets.
ollama pull llama3.3:70b		43.1 GB2×24 GB GPUs or 48 GB card
Mac: 64 GB 统一内存		128KLlama Community

Unlocking plant knowledge without exporting it?

We help manufacturers deploy AI inside the plant boundary — corpus design over maintenance and quality records, correct placement in the zone model, hardware sizing for plant conditions, and grounding that respects document control. Vendor-neutral and practitioner-level.

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常见问题

What is the most valuable manufacturing AI use case to start with?
Making institutional knowledge searchable. Maintenance histories, deviation reports, shift handovers, and equipment manuals hold decades of hard-won troubleshooting knowledge that is effectively inaccessible under time pressure, and that walks out the door as experienced staff retire. A retrieval system over that corpus gives a technician on a night shift the answer a veteran would have given, and it pays back faster than any predictive-maintenance project because the data already exists and needs no new instrumentation.
Can an AI system touch the control network?
It should not, and the segmentation you already have is the reason. Language models belong on the IT side, reading operational history through defined conduits and producing advice for people. Anything that writes to a control system crosses a boundary that exists for safety reasons and brings the deployment into scope of a very different assessment. Keeping inference advisory and read-only is not a limitation of the technology so much as the correct placement of it in a plant that has been designed properly.
How do we keep process knowledge from leaking into a vendor's systems?
Run the model where the data is. Trade secret protection depends on demonstrating reasonable measures to preserve secrecy, and sending formulations or process parameters to an external service is difficult to reconcile with that — regardless of the contract terms. On-premise inference with no outbound route from the model host makes the answer a matter of network configuration rather than vendor trust, which is a much stronger position if the question is ever litigated.
Does an AI assistant interfere with a quality management system?
Only if you let it answer without citing the document revision. Quality systems depend on people working from the controlled version in force, so an assistant that produces a confident answer from a superseded SOP is an audit finding. Ground every answer in the controlled document set, show the document identifier and revision alongside the answer, and re-index when documents change. Done that way, the assistant strengthens document control by making the current revision easier to find than the printout on the wall.
What hardware does a plant need?
Modest, and it fits in the IT rack you already have. A single 12–24 GB GPU running a 14B or 32B-class model handles retrieval and drafting for a site, because usage is intermittent — technicians ask questions in bursts rather than continuously. Multi-site deployments consolidating several document sets justify a 48 GB card. Environmental fit matters more than raw performance here: power, cooling, and dust in a plant IT room constrain the choice more than benchmark scores do.

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