I've spent thirty years
keeping pipelines running.
Now I build the AI that understands them.

A nine-stage computer-vision and spatial-database pipeline that converts scanned piping & instrumentation diagrams into a queryable process graph — components, pipes, tags, and connectivity. The core extraction path uses no machine learning at all. That is the design decision, not a limitation.

A visual, node-based pipeline builder that puts language models to work on the hardest manual step in any Industry 4.0 program: deciding what every tag in every PLC actually represents. It ingests real Rockwell/Allen-Bradley exports, classifies equipment, generates descriptions, and proposes the namespace hierarchy.

A real-time voice assistant that answers phone calls, transcribes live, and responds conversationally — and, more to the point, proactively calls a human when a natural-gas facility raises a critical alarm, describes the condition out loud, and records the verbal acknowledgment.

A self-hosted, OpenAI-API-compatible router that answers every request with a cheap local model first, has a second model grade that answer against a rubric, and calls a paid frontier model only when the local answer measurably fails.