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.

Logistics & Mobility · Predictive maintenance & quality

Fleet and handling-equipment reliability

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

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Unplanned downtimeMean time between failuresMaintenance cost per assetDefect escape rate

The problem

What this fixes

A breakdown in the middle of a round costs the delivery, the driver's day and the customer's promise, and telematics data goes unused beyond speeding reports.

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.

Logistics & Mobility

Fleet condition monitoring

Telematics and service history combined into a failure-risk score.

Logistics & Mobility

Forklift and conveyor uptime

Handling equipment monitored where a stoppage halts the whole dock.

Logistics & Mobility

Maintenance scheduled around routes

Work planned into low-demand windows instead of peak days.

Logistics & Mobility

Parts availability

Stock aligned to predicted failures per depot.

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.