We build agentic AI for the work that runs manufacturing — from demand sensing, supplier lead times, and safety-stock management to processing engineering artifacts, validating specs, and optimizing document-heavy workflows. Agents handle the volume. Your team keeps the call.
A confidently wrong value — on an engineering spec, in a demand forecast, in a supplier lead-time assumption — isn't a typo. It's a tooling change, a stockout, a supplier dispute, a recall risk. Most enterprise AI demos sidestep this by sticking to chat. We don't have that luxury. Our outputs flow into engineering systems, ERP transactions, supplier portals, and quality records. Every field has to be defensible.
Five principles run through everything we build — whether an agent is reasoning over an engineering drawing or a demand signal. They are how we get AI past the pilot stage and into the parts of the factory where decisions actually live.
Domain experts sit at the points where judgment is irreducible — a spec callout, a demand override, a supplier exception. The agent does the volume work; the human keeps the authority.
Some problems still belong to traditional ML — anomaly detection, time-series forecasting, classification at scale. We use ML where it's already the right answer, LLMs where they unlock something new, and both together when the work calls for it.
Most tasks don't need the biggest model. Each step of a workflow routes to the cheapest model that still gets it right — small models for classification and routing, mid-sized for extraction, frontier models reserved for the reasoning that actually needs them. Margins on AI-heavy work live here.
Every value an agent produces — an extracted dimension, a reorder quantity, a lead-time estimate — carries an explicit confidence. Low-confidence items are flagged, not hidden. Reviewers see exactly what the system was unsure about and why, instead of being asked to re-audit everything.
Every agent response is bound to a schema before it leaves the model — typed fields, validated units, output ready for the engineering record or the ERP entry it's headed into. If the structure doesn't validate, the answer doesn't ship.
Engineering artifacts are among the hardest documents in any operations stack — multi-page, multi-format, full of nested callouts, format-specific conventions, and decades of accumulated tribal knowledge. Most extraction tools give up on them.
We pull validated data out of the artifacts most teams still transcribe by hand, with confidence-flagged review built in. The output flows directly into validation frameworks, supplier systems, and quality records — without a hand-typed step in between.
We partner with manufacturing leaders from pilot to production, with measurable operating impact. If the work above maps to something you're facing, we'd like to hear from you.