Generative AI: Manufacturing

Generative AI in Manufacturing

Copilots that help technicians fix the machine, quality engineers close the CAPA and planners understand the shift, using your manuals, work orders and production data rather than the open internet. Language models advise and draft; they never write to a PLC. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

The use cases that pay back first in manufacturing are maintenance copilots, quality and CAPA drafting, work instructions and supplier document extraction.
Copilots answer from your OEM manuals, SOPs and CMMS work order history, with a citation for every step they suggest.
Generative AI stays out of control loops and safety functions. It reads from OT through a DMZ and never writes to machines.
Drawings, recipes and export controlled data can stay on self hosted models inside your network.
A proof of concept usually takes about 4 weeks; a production copilot integrated with your CMMS or MES usually takes 10 to 16 weeks.

Why manufacturing AI projects stall, and what is worth solving

Plants hold decades of knowledge in manuals, work orders and the heads of experienced people. Generative AI can make it usable, but only if it is grounded in plant data and trusted by the people on the floor.

Know how retiring with the senior technicians. The person who knows why press 4 trips on a cold Monday is two years from retirement. New technicians search binders and PDFs while the line waits.

Work order history nobody can search. Years of CMMS records written as brg chg or pump noisy, in three languages and four spelling styles. The answer to today's breakdown is in there, but no keyword search will find it.

Quality paperwork that takes longer than the fix. 8D reports, NCRs and CAPA records are written by hand from inspection data and emails. Similar past nonconformances are rarely found, so root causes repeat.

Sensor data without context. Historian tags named after PLC addresses mean nothing to a language model. Projects that point an LLM at raw time series data produce fluent nonsense and lose the plant's trust.

IP and export control worries. Drawings, process recipes and ITAR or EAR controlled technical data cannot be pasted into a public chatbot. Without an approved hosting model, legal stops the project at week one.

Pilots built in IT, not on the floor. A chatbot on the intranet that cannot see the CMMS, the MES or the machine's actual history gets tried once by technicians and then ignored.

Use cases

Generative AI use cases we build for manufacturing

Each use case is grounded in your plant documents and systems, tested on a golden dataset of real faults, NCRs and documents, and keeps engineers in charge of every decision.

Maintenance copilot

RAG over OEM manuals, SOPs and CMMS work order history in SAP PM or IBM Maximo gives technicians troubleshooting steps with citations on a tablet at the machine. When a predictive maintenance model raises an alert, the copilot explains it and drafts the work order for a planner to approve.

Quality and CAPA agent

Drafts NCRs and 8D reports from inspection results, SPC data and complaint emails, finds similar past nonconformances and their root causes, and proposes containment actions. A quality engineer edits and signs every record.

Work instruction generation

Turns engineering documents, process sheets and recorded expert walkthroughs into step by step digital work instructions with images, translated for a multilingual workforce. Instructions are versioned and released through your MES or document control.

Technical document extraction

OCR plus LLM extraction for supplier certificates of conformance, EN 10204 material certificates, drawing title blocks and BOMs. Values are checked against purchase order and specification limits, and exceptions go to a review queue before posting to the ERP.

Production data copilot

Supervisors ask why OEE dropped on line 3 on the night shift and get a text to SQL answer over OEE, downtime and historian data mapped to an ISA-95 asset model, with the query shown. Read only, and only on contextualised data.

Procurement and supplier agent

Extracts and compares supplier quotes against specifications, tracks delivery promises from supplier emails, and flags late or changed orders to buyers with a drafted response. Purchase orders are still released by people in the ERP.

How we deliver

From one line to the whole plant

01
Use case discovery and data readiness
We walk the floor, sample manuals, work orders, NCRs and supplier documents, and check access to CMMS, MES and ERP. Output: a ranked use case list and an approved hosting model for your IP.
02
Evaluation harness and prototype
A golden dataset of real faults, quality cases and documents with acceptance criteria set by your engineers, then a prototype tested against two or three candidate models, including self hosted ones.
03
Guarded pilot with human in the loop
Live with one maintenance crew, line or quality team. Every suggestion carries citations, drafted work orders and records are approved by a person, and technicians rate answers so the dataset grows.
04
Production rollout with monitoring
Roll out by line and site behind a feature flag, with accuracy, usage and cost dashboards, drift alerts, and quarterly model reviews. Your team is trained to maintain the document index.

AI governance for manufacturing

Manufacturing AI governance is about three things: keeping AI out of safety functions, protecting IP and controlled data, and keeping quality records trustworthy.

EU AI Act EU Machinery Regulation 2023/1230 IEC 62443 aligned OT security ITAR and EAR export controls ISO 9001 and IATF 16949 records FDA 21 CFR Part 11 (regulated plants) GDPR and UK GDPR Self hosted model option

Copilots advise, they do not control. Generative AI reads production data through the same DMZ pattern we use for IIoT and never writes to PLCs or safety systems. The EU Machinery Regulation, which applies from 20 January 2027, brings safety components with self evolving behaviour into stricter conformity assessment, which is one more reason to keep language models out of safety functions.

Your IP stays inside approved boundaries. Drawings, recipes and process parameters go only to models under enterprise terms that exclude training on your data, in a region you approve. ITAR or EAR controlled technical data stays on self hosted open weight models inside your network.

Quality records stay human signed. AI drafts NCRs, CAPAs and work instructions, but an engineer reviews and signs each record, and the system keeps the draft, the edits and the sources. Where FDA 21 CFR Part 11 or EU GMP Annex 11 applies, audit trails and electronic signatures are in scope from the start.

Models and tooling we use for manufacturing AI

Portable language models with a self hosted path, retrieval over plant documents, and adapters for the maintenance, execution and ERP systems you already run.

Claude, GPT-5 and Gemini Llama 4 and Qwen (self hosted) Azure OpenAI and AWS Bedrock LangGraph pgvector and Qdrant AWS Textract and Azure Document Intelligence SAP PM and IBM Maximo integration SAP S/4HANA integration TimescaleDB and InfluxDB OPC UA via edge gateway Python and PyTorch Langfuse

FAQ

Common questions

How much does a generative AI project in manufacturing cost?

It depends on scope, so we quote from a written estimate rather than a price list. Most plants start with a fixed price proof of concept for one use case, such as a maintenance copilot for one asset class, then move to a production build or a dedicated engineer. Model API or GPU costs are passed through at cost. Our pricing page lists current entry points.

Will our drawings and process data be used to train someone else's model?

No. We use enterprise model APIs that contractually exclude training on your data, in a region you approve, or self hosted open weight models on your own infrastructure. Export controlled data stays on self hosted models only.

How do you stop a maintenance copilot giving a wrong or unsafe answer?

By grounding, citations and limits. The copilot answers only from your approved manuals, SOPs and work order history, shows the source for every step, says when it does not know, and never overrides lockout tagout or safety procedures. A golden set of real faults agreed with your engineers runs on every change.

How long does a first manufacturing AI project take?

Usually about 4 weeks for a proof of concept and 10 to 16 weeks for a production copilot integrated with your CMMS or MES. The biggest variable is how organised the manuals and work order history are.

Should we use the AI features in our CMMS or ERP, or build our own?

Use the vendor's AI where it covers the task well and build where your data spans systems. Vendor assistants work inside their own product. When a technician needs OEM manuals, CMMS history and live machine context in one answer, or data must stay on premise, a custom build on portable models usually fits better.

Do we need a data platform before we can use generative AI?

Not for document based use cases. Maintenance copilots, CAPA drafting and certificate extraction work on documents and CMMS records you already have. Production data copilots do need contextualised data, which our IIoT work provides.

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