Risk-based asset management with AI in the loop.

Pipelines, traffic infrastructure, tunnels, industrial equipment. We build the models, the integrations and the run-book so your operations team trusts the signal before it hits their screen.

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Symptoms

The pain you are likely feeling.

Asset inspections are calendar-driven, not condition-driven, which means you over-inspect what’s fine and under-inspect what’s failing.
You’ve tried AI pilots on sensor data but nothing reached production because operations couldn’t trust the signal.
Every regulator asks for a different risk model and you have to rebuild from scratch each time.
The data is there — SCADA, sensors, inspection reports, helicopter imagery — but it lives in six different systems with no shared schema.
A model that scores 90% accuracy in the lab still gets ignored on the screen of the operator who has to act on it.

Approach

How we approach it.

Start with the asset taxonomy, not the model.Two weeks mapping how you actually segment the asset, how risk is currently quantified, who acts on what. The data architecture follows that, not the other way around.
Combine the sources.SCADA, inspection databases, GIS, imagery — into one OneLake medallion with a shared asset key. The model only sees clean, governed inputs.
Models the operators trust.Explanations alongside scores, confidence intervals, comparison with the manual risk model. Operators see why the AI flagged this segment, not just that it did.
Run-book before go-live.What does an operator do when score crosses threshold? Who escalates, who closes the loop, how do we know the model is still calibrated six months later? Documented, agreed, signed off.
Compliance and audit baked in.Risk decisions logged, model versions tracked, retraining triggers defined. EU AI Act-ready by default, not retrofitted.

Proof

Where this signature landed.

FluxysComputer vision and risk analytics on 450,000 pipeline segments, scoring each by risk × impact for maintenance prioritisation.

Kennedy TunnelOperational AI on tunnel monitoring, supporting traffic and safety operations.

SchréderPredictive analytics on lighting asset health, turning sensor telemetry into maintenance signals operators act on.

Deliverables

What we typically ship in 90 days.

Unified asset-and-condition data model in OneLake, with the existing systems (SCADA, inspections, GIS) cleanly integrated.
First risk-scoring model running on one asset class end-to-end: training pipeline, scoring service, monitoring dashboard.
Operator-facing interface that shows the score, the reasons, and the recommended action. Built in Power BI or your existing operations platform.
Run-book: what happens when score crosses a threshold, how to handle false positives, how to retrain when conditions change.
Compliance documentation aligned with the EU AI Act tier that applies to your use case.

Sitting on a fleet of assets and an operational team that does not trust the AI dashboard?

Bring us one asset class. We’ll walk through what trustworthy operational AI looks like for it, and what 90 days of build would deliver.

Book a discovery call