LLM.co · Private AI & Custom AI DevelopmentPrivate AI for manufacturing

AI for manufacturing that stays inside the plant

Most of what a plant knows is written down somewhere. It sits in OEM manuals, CMMS work orders, engineering change orders, drawings, inspection records and shift notes. The rest sits with senior technicians who will retire in the next few years. Useful AI for manufacturing starts with that material and makes it answerable at the machine.

That material is also what competitors would most like to see. Process parameters, tooling designs, supplier pricing and customer drawings are trade secrets, and some of them carry ITAR or customer confidentiality terms. LLM.co builds private AI for manufacturers on open-weight models that run on plant servers, in your own cloud account or on an isolated OT segment. Nothing goes to an outside model provider.

Workflows worth automating on the plant floor

The best first projects are narrow, frequent and already documented. These are the ones we see most often in custom AI development for manufacturers.

• Maintenance troubleshooting. A technician describes the fault and gets likely causes and steps drawn from the manual and past work orders on the same asset, with each source cited.
• Work order cleanup. Free-text CMMS entries are classified by failure mode, cause and part, so reliability engineers can trust the history.
• Engineering search. Drawings, ECOs, test reports and deviations become searchable in plain English, filtered by part number and revision.
• Supplier documents. Certificates of conformance, material certs and specs are extracted on receipt and checked against the purchase order and drawing.
• Quality records. Nonconformance reports and CAPA write-ups are drafted from inspection data for a quality engineer to review and sign.

Where predictive maintenance fits

Predictive maintenance usually means sensor models that spot a bearing or motor heading toward failure. Those models need clean, labeled history, and many plants find that their CMMS records are too inconsistent to label against. A language model can fix that first. It reads years of work orders, normalizes the failure codes and links each repair to the asset and the date.

Once the history is clean, sensor-based models have something reliable to learn from. A maintenance assistant can also pull that history into the conversation when a technician asks about a recurring fault. We build the language side and work alongside your controls or data team on the sensor side.

Running on-premises AI next to the OT network

Plant networks follow zone and conduit rules for good reason. On-premises AI for a plant normally runs in the site data center or a DMZ, reads exports from MES, historian and CMMS systems, and never writes directly to controllers. Where a use case must sit inside an OT zone, we deploy to edge hardware with no internet route and ship model updates as signed bundles through your change process.

Users sign in through your identity provider. Answers respect the same folder and site permissions your file shares already use. Every question, retrieval and answer is logged so your security team can review it.

What to pilot first and what to avoid

A good pilot is one asset class or one production line with a few years of work orders and a complete set of manuals. Your technicians help build the evaluation set from real faults they have already fixed, so quality is scored before anyone relies on it.

Avoid projects that let a model change setpoints or release product without a person. Avoid starting with scanned drawings from decades ago until the newer records work well. Avoid any vendor whose tool needs your drawings uploaded to their cloud.

How an engagement runs

An AI for manufacturing project starts with a two-week discovery sprint to rank use cases, inventory data and size hardware. A focused first system usually reaches production in eight to twelve weeks. You own the code, prompts, evaluation sets and any fine-tuned weights, and each phase ends with a go / no-go decision.