Who we are
Built by operators, not data scientists.
The gap between a working demo and a running plant is not a model problem. It is a knowledge and experience problem.
Who we are
The gap between a working demo and a running plant is not a model problem. It is a knowledge and experience problem.
Every operation runs on two sets of rules: documented procedures and the operating knowledge your senior people carry but never transfer. The calls they make before the instruments confirm them. The limits that shape throughput in ways the permit does not explain.
AI sees the first set. It misses the second. We have seen this failure from the inside, across refining, chemicals, metals, and automotive. Closing that gap is the work. If you have seen it in your operation, we want to hear about it →
Which instruments to trust, where the SOP runs out, and how regulatory limits actually shape throughput.
The furnace conditions the design didn’t anticipate, and the QC calls made before the lab confirms them.
NVH that surfaces at integration, and launch decisions that live between engineering release and what the line can actually build.
The gap between what works in the lab and what holds up at full scale. And the decisions that can’t be undone.
Throughput an operation can’t sustain, and risk that lives in the shift change, not the balance sheet.
What it takes to run AI reliably when someone is trying to break it: anomaly detection for ICS, audit trails that survive a breach, fault-tolerant compute.
If you’re closing this gap in operations, we’d like to hear how.