How much history do you need?

Enough to contain the failure modes you care about, often a year or more of sensor data with credible maintenance records. If it is not there, we start with anomaly detection and build the labels forward.

Can it run at the edge?

Yes, and for high-frequency vibration or vision it must, bandwidth, not accuracy, is the deciding constraint.

Healthcare & Life Sciences · Predictive maintenance & quality

Medical equipment and facility reliability

Catch the failure and the defect before they reach the customer.

Book the Data Foundation Assessment

Unplanned downtimeMean time between failuresMaintenance cost per assetDefect escape rate

The problem

What this fixes

An imaging scanner or a chiller failing unannounced cancels a day of appointments, and maintenance is still scheduled by calendar rather than by condition.

Condition monitoring and remaining-useful-life models on sensor, vibration, thermal and process data, plus visual inspection models on the line. Alerts arrive as work orders in the maintenance system with the evidence attached, so a planner can act without opening a data tool.

Healthcare & Life Sciences

Imaging and lab equipment monitoring

Usage, error logs and service history combined into a failure-risk signal.

Healthcare & Life Sciences

Critical facilities

Chillers, medical gas and power monitored where an outage becomes a clinical incident.

Healthcare & Life Sciences

Maintenance planning

Work orders raised against condition, scheduled around clinic sessions.

Healthcare & Life Sciences

Spare-parts readiness

Stock aligned to the assets most likely to need it.

In this sector

Where it earns its place

Delivery

How we build it

Foundation check

Before any build

We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.

Narrow first release

First release

One workflow, one team, real traffic, measured against the baseline we captured before we started.

Controls and evaluation

Runs in parallel

Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.

Scale and hand over

After sign-off

Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.

Data foundation

What has to be right first

Time-series sensor data at a sampling rate that can actually show the failure mode.
Maintenance history with real failure codes, not 'other'.
Asset criticality so alerts can be prioritised.
Labelled defect images for any visual inspection model.

Controls

Guardrails and KPIs

Every action is logged and replayable

Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.

Accuracy is monitored, not assumed

A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.

A human owns every consequential decision

Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.

Data stays where your regulator requires

In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.