01 / AI-first
2025 · Confidential · Prototype
Industrial Intelligence
Institutional memory for heavy industry: ingest maintenance documents, build a knowledge graph of assets, people, incidents, and parts, then answer field questions with citations.
Confidential
- 01
Five-agent ingest-to-graph pipeline
- 02
Deterministic single-point-of-failure expertise scoring
- 03
Cited answers over plant documents
- 04
Interactive knowledge graph plus Risk Radar
The brief
When a senior plant engineer retires or leaves, the institutional knowledge of which machine has which failure history, which procedure applies, and who is the only person who can fix a specific asset leaves with them. That knowledge usually lives in scattered documents and one person's memory, not in a searchable system.
The outcome
- 01Answers "if this engineer retires tomorrow, which machines lose their only expert?" deterministically, not as a best-guess LLM answer
- 02Full document-to-graph pipeline: PDF/DOCX/XLSX ingestion → entity extraction → graph linking, in one flow
- 03Built and demoed end-to-end in a hackathon timebox on a five-agent tool-use loop
The build
A five-agent pipeline (ingest, entity-link, jargon-normalize, graph-traversal, risk-scoring) turns uploaded maintenance documents into a live knowledge graph of Assets, People, Incidents, Documents, Procedures, and Parts.
On top of the graph sits a Risk Radar that flags machines that depend on a single expert, and an Expert Copilot that answers plant-floor questions with citations back to the source document.
Specs
- Platform
- Web, chat interface + interactive knowledge graph + Risk Radar panel
- Graph engine
- In-memory graph (graphology), persisted per plant
- Agents
- Ingestor, Linker, Jargon-normalizer, Traversal, Risk-scorer
- Ingestion
- PDF, DOCX, XLSX parsing → entity extraction → graph linking
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