Most industrial AI stalls at the pilot. We embed senior engineers who take AI systems from sensor data to a running line, with the evals, monitoring, and safety work that keeps them running.
Why this is hard
The model is rarely the problem. The system around it is.
A model that scores well in a notebook still has to survive missing sensor reads, a network that drops, and an operator who needs an answer in two seconds.
Data pipelines, evals, monitoring, rollback, and access control are most of the work. They are also the first things scoped out to hit a pilot deadline.
Output validation and audit trails added after the fact rarely survive a real compliance review. They have to be in the design from the start.
What changes the outcome
Three things decide whether an industrial AI system ships.
A delivery lead plus senior engineers in your repo within 5 days. No junior bench, and no handoff between a strategy team and a build team.
Evals, observability, and rollback designed in before the first model call. We have shipped over 100 products to production.
How the work runs
Prove it, ship it, then operate it. Each stage feeds the next.
In production
Unplanned equipment failures were costing this manufacturer $2M per incident in lost production. We deployed IoT sensors and built ML models that predict failures 72 hours in advance, enabling scheduled maintenance that keeps production lines running.
Outcome
Our clients
Real systems for field operations, telemetry, and connected hardware.
Trusted by engineering teams at
Where to start
One industrial use case, working on your data, in weeks.
Pick the use case that costs you the most. In two to four weeks you get a working prototype on your data, an eval set that says whether it is good enough, and a straight answer on what the production build takes. Sprints run $15K to $50K.
What you walk away with
From our engineers
How our engineers think about agents, evals, and AI security.
Talk with the engineers who would build it, not a salesperson. Bring the use case that costs you the most.
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Industrial AI is the use of machine learning and language models inside physical and operational systems: production lines, connected equipment, field operations, and the data they generate. It differs from general enterprise AI because the inputs are sensor and telemetry data, and a wrong answer can stop a line or create a safety risk.
