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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.

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

What this fixes.

  • —

    Unplanned line stoppage = six-figure event

  • —

    Schedule-based maintenance over-services

  • —

    Failure precursors invisible in dashboards

CAPABILITIES
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.

EXAMPLE
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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MIRROR·tested on us first · 100%AUTOMATIONS·50 ready to shipCATEGORIES·10 business functionsLIVE PRODUCTS·2 of our ownSTUDIO·Vienna, AT · since 2019MIRROR·tested on us first · 100%AUTOMATIONS·50 ready to shipCATEGORIES·10 business functionsLIVE PRODUCTS·2 of our ownSTUDIO·Vienna, AT · since 2019
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