Agentic diagnostics

Diag

Diagnose and troubleshoot with confidence. Investigate, analyze and act on your assets with AI-powered insights.

01The diagnostic

From alarm to answer, with a human in the loop.

Press Investigate on any ticket. Millwright opens a workspace, pulls the evidence, and works the fault out loud, on a board your whole team can see.

01

Builds the case

The agent reads the ticket, finds the alarm behind it, and lays the trend, the working theory and what it has ruled out onto a board. The reasoning is something you can point at.

MediumMB6-A2

CCW supply header is warmer than its limit

1CC-TE-0341 · 104.6 °F, limit 104 °F · 7h 40m

Nobody has picked this up yet

Alarm

1CC-TE-0341 · CCW supply header

OVER LIMITSHUTDOWN ↑10210310400:0006:0011:45
Chart

Working theory: the reading has been over the limit since the alarm came in. Either the header really is warmer, or the element measuring it has drifted.

Note

02

Knows when to ask

Telemetry cannot tell a drifting instrument from a drifting process. When the data runs out, the run stops and asks the person standing at the machine, with the right procedure already open.

03

Closes the loop

The new work order keeps its link to the alarm it resolves, the discounted theories stay on the record, and the investigation is one press from a drafted article for the next crew.

02Why now

Detection was never the bottleneck. Diagnosis is.

A decade of money went into anomaly detection, and it worked — most plants now flag more anomalies than they have engineers to read them. Every one still lands in a queue for somebody to work out by hand. That is the part this closes, and none of the four things that make it possible is a better model.

Every conclusion shows its evidence

The points it read, the document it cited, and the theory it ruled out — all on the record, all auditable.

Not a score, with no reasoning attached

The investigation is the work order

Raised from the board and linked to the alarm it answers, so a closed ticket can say what it closed.

Not a dashboard, beside a CMMS somebody retypes it into

Day one is read-only, on one system

That is the whole first step. It fits inside one engineer's authority, and it does not wait on a data programme.

Not eighteen months of ingestion before anybody sees a fault

It stops and asks when the data runs out

Telemetry cannot tell a drifting instrument from a drifting process. On that fault, asking is the only safe answer.

Not a model with no way to say it is out of its depth

See it running on your own plant.

We are onboarding a small number of operations teams. Three questions, and we will be in touch.