Customers

Trusted by maintenance teams across manufacturing, energy, and utilities

From CNC floors to offshore compressors, DiagAI helps industrial operators detect failures earlier, diagnose root causes faster, and eliminate unplanned downtime.

87.3%
Anomaly detection F1
84.6%
RCA success rate
<2s
Alert-to-diagnosis
3+
Industries in production

Companies running DiagAI in production

Apex Automotive
Manufacturing
NovaDrill
Oil & Gas
GridForce Energy
Utilities
PrecisionWorks
Manufacturing
Atlantic Pipeline
Oil & Gas
Volta Grid
Utilities
Forgemaster
Manufacturing
Crestline Power
Utilities

Case studies

Real deployments, real results.

Apex Automotive

Manufacturing

43% reduction in unplanned downtime across 12 CNC lines

Apex Automotive deployed DiagAI across their stamping and CNC machining floor. Within 90 days, the anomaly detection pipeline had flagged 17 early-stage spindle faults that would have caused unplanned stoppages. Total avoided downtime cost in the first year: $1.4M.

DiagAI's RCA reports give our maintenance team a clear starting point every time. We used to spend hours tracing faults — now it's minutes.

Head of Reliability, Apex Automotive
43%
Downtime reduction
17
Faults caught early
$1.4M
Avoided costs (Y1)
12
Production lines

NovaDrill

Oil & Gas

Zero undetected compressor failures in 18 months of production

NovaDrill integrated DiagAI's API with their SCADA system to monitor 9 compressor signals in real time. The knowledge graph's SWRL rules for corrosion and seal degradation patterns proved especially accurate in their offshore environment, producing zero false-negative alerts across 18 months.

Offshore compressor failures are expensive and dangerous. DiagAI gives us confidence that nothing is slipping through the cracks.

VP Operations, NovaDrill
0
Undetected failures (18mo)
9
Monitored signals
99.1%
Alert precision
18mo
Continuous operation

GridForce Energy

Utilities

Multi-domain failure detection across wind turbines and substations

GridForce deployed DiagAI for both wind turbine drivetrain monitoring and substation transformer health. The cross-domain knowledge graph transfer meant that failure patterns learned on turbine gearboxes also improved transformer insulation anomaly detection — without retraining.

The cross-domain learning is genuinely impressive. We didn't expect turbine fault patterns to improve our transformer anomaly detection, but they did.

Chief Engineer, GridForce Energy
Asset types monitored
31%
Maintenance cost reduction
84.6%
RCA accuracy on novel faults
<2s
Alert-to-diagnosis latency

What our customers say

The multi-agent reasoning is what sets DiagAI apart. It doesn't just say something is broken — it tells you why, what to do, and what will happen if you wait.

R
Reliability Engineer
PrecisionWorks

We were sceptical about an AI system understanding our specific failure modes. The knowledge graph customisation changed that completely.

P
Plant Manager
Forgemaster

Integration with our existing CMMS took less than a day using the REST API. The docs are genuinely good.

I
IT Systems Lead
Atlantic Pipeline

Sub-2-second RCA on a live sensor stream — I didn't think that was possible before we saw the demo.

S
Senior Maintenance Engineer
Crestline Power

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