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Industry-Specific·6–16 weeks·10–16 weeks

Predictive Maintenance

For manufacturing, energy, logistics fleets. Sensors stream telemetry; ML model learns failure precursors per asset type. Maintenance team gets days-to-weeks lead time on failures, replaces sample-based maintenance with condition-based.

Unplanned downtime
−45%
Maintenance cost
−22%
Asset life
+12%
The problem

What this fixes.

  • Unplanned line stoppage = six-figure event

  • Schedule-based maintenance over-services

  • Failure precursors invisible in dashboards

How it works

Three jobs, on rails.

Ingest

Sensor data

Field telemetry unified into one pipeline.

Predict

Per asset

Failure probability + remaining useful life.

Schedule

Auto-PM

Maintenance work orders raised on predicted threshold.

From signed proposal to live

The path.

01

Inventory the assets that matter and the sensors already on them.

02

Pipe telemetry into the model with at least 6 months of history per asset class.

03

Calibrate per-asset thresholds with the maintenance lead.

04

Switch from calendar PM to condition-based PM on the highest-impact assets first.

A real moment

One scenario, one outcome.

The scenario

A bottling line's filler-head bearing shows a 4Hz vibration anomaly two weeks before it would normally fail.

The outcome

Maintenance scheduled for the next planned changeover. Zero unplanned downtime, saving an estimated €180,000 in lost production.

Engagement

Scoped on a call.

Delivery

10–16 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

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