Consulting practice focused on R&D labs, validation teams, and industrial groups. Apply AI agents and self-hosted AI to equipment programming, knowledge capture, and validation pipelines that previously demanded months of specialist time.
Why use AI for industrial work at all? Fair question. The hype around the term doesn't answer it on its own — the four practice areas below do. Each shows up as speed, as captured knowledge, and as workflow automation past what a single team can run by hand.
Agents that turn natural-language requests into validated automation scripts for CNC, PLC, semiconductor, and robotics equipment. RAG over your documentation, allowlisted safety, self-correcting debug loop.
On-premise systems that capture the undocumented know-how living in your senior engineers' heads — equipment quirks, calibration steps, troubleshooting paths — into a searchable, AI-ready knowledge base.
End-to-end automation across instruments, simulators, and post-analysis tooling. Cross-domain workflows that turn months of manual validation into repeatable, scaled processes.
Stand up LLM systems with vector databases and retrieval pipelines — local when data can't leave your perimeter, cloud when latency or scale demand it, hybrid when both. Model selection, security guardrails, air-gapped builds where required.
Engineers describe outcomes in natural language. No vendor-specific syntax to learn or relearn for each new instrument.
Calibration routines, conformance suites, and validation pipelines that took weeks to author and debug now run from a single prompt.
One agent stack handles G-Code, OPC UA, Modbus, SECS/GEM, and vendor robotic APIs. Add new equipment by uploading its documentation.
Every generated command checked against an equipment-specific allowlist. Scripts validated in sandbox before touching production hardware.
Four weeks from a first conversation to a production-ready deployment in your environment. No long-term contracts. The deliverable belongs to you.
Audit your equipment stack, protocols, and existing scripts. Define safety boundaries and allowlists.
Ingest equipment documentation, datasheets, and historical scripts. Tune retrieval for accuracy.
Stand up the agent with your protocol adapters. Validate against simulators or sandboxed equipment.
Deploy on the floor with one team. Train operators, monitor results, document and hand over.
Whether the equipment is a CNC mill, a PLC cabinet, a wafer prober, or a robotic cell, the deliverable is the same: validated, auditable code an operator can trust on day one.
The shortest path to seeing this work is a focused engagement on a single piece of equipment. See the worked example, or get in touch directly.