Generative AI in manufacturing that turns buried knowledge into answers at the point of need.

Generative AI delivers the most value in manufacturing for: (1) Technical documentation, generating, updating, and maintaining SOPs, work instructions, maintenance procedures, and quality documentation from engineering data and subject matter expert input. Documentation that takes months to produce manually can be accelerated dramatically. (2) Equipment troubleshooting support, LLMs connected to equipment manuals, maintenance history, and fault codes provide first-line diagnostic support to maintenance technicians, reducing mean time to repair. (3) Quality reporting, automating the generation of quality reports, non-conformance reports, and corrective action documentation from inspection data, reducing reporting time for quality engineers. (4) Knowledge management, capturing and surfacing the tacit knowledge of experienced workers through conversational interfaces, reducing knowledge loss from retirements and turnover.

We build a RAG (retrieval-augmented generation) system that connects an LLM to your equipment documentation, OEM manuals, maintenance procedures, past maintenance records, and fault code databases. When a maintenance technician describes a symptom or fault code, the system retrieves relevant documentation and provides specific diagnostic steps, likely causes based on historical patterns, and recommended remediation. The system references source documents so the technician can verify the guidance. This reduces the time to first diagnostic action and helps newer technicians access the expertise that's currently only available in experienced technicians' heads.

Manufacturing process data, production parameters, quality data, equipment specifications, and proprietary procedures, is typically sensitive IP. We use private LLM deployments (Azure OpenAI, AWS Bedrock, or Anthropic Claude on private infrastructure) with data processing agreements that prohibit training on your data. On-premises LLM deployment is available for manufacturers with strict data residency or air-gap requirements. Documents are processed and indexed within your infrastructure; only the retrieval context is sent to the LLM for each query. We confirm the appropriate architecture based on your data classification and security requirements during scoping.

An equipment troubleshooting assistant with RAG over your maintenance documentation and fault code database typically runs $25,000 to $60,000. A technical documentation generation system producing SOPs and work instructions from engineering data typically runs $30,000 to $70,000. A full manufacturing AI platform covering troubleshooting, documentation, and quality reporting typically runs $60,000 to $140,000. Cost depends on source documentation volume, system integrations (CMMS, MES, QMS), and deployment architecture. We scope every project before pricing it.

A focused troubleshooting assistant or documentation generation system typically takes 8 to 12 weeks from signed scope to production. More complex platforms covering troubleshooting, documentation, and quality reporting run 14 to 20 weeks. Scope is fixed and price is locked before development starts, so timeline estimates are reliable.

We integrate with the systems manufacturing teams already use. CMMS platforms including IBM Maximo, SAP PM, Infor EAM, UpKeep, and Limble. QMS platforms including SAP QM, ETQ Reliance, MasterControl, and Intelex. ERP systems including SAP S/4HANA and Oracle. We also ingest unstructured sources: OEM manuals in PDF, legacy SOPs in Word, CAD-derived BOM data, and IoT historian data. Integration approach is confirmed during the scoping week.