Turn sensor data into failure predictions. Custom-built predictive maintenance for manufacturing and industrial IoT — fewer breakdowns, less unplanned downtime, and a model your team actually owns.
Last updated: 11 June 2026
Predictive maintenance AI reads sensor and PLC data from your machines, learns each asset's failure modes, and flags degradation weeks before breakdown — cutting unplanned downtime. Crux builds it custom: a €2,500 audit scopes the data, a €20,000 proof of concept proves prediction on one asset class, and production runs from €50,000. You own the code.
Predictive maintenance AI models the condition of each machine from its own signals — vibration, temperature, current, pressure, acoustic and PLC/SCADA logs — and forecasts when a component will drift out of spec. Instead of a fixed service calendar or a costly breakdown, your team gets a ranked list of assets, the likely failure mode, and the estimated time to failure. It is a custom build wired to your line, not a dashboard bolted on top. See how we implement AI end to end.
Every project follows the same signal path. 1. Sensors & data pipeline: we tap existing PLC/SCADA tags and add sensors only where the physics demand it, then stream to a time-series store with clean timestamps and units. 2. Feature engineering: raw signals become features — FFT bands and RMS for vibration, rolling statistics, thermal gradients, duty-cycle context — labelled against your maintenance and breakdown history. 3. Model: we start with interpretable anomaly detection and survival / remaining-useful-life models, escalating to deep models only when the data earns it, and validate on held-out failures the model has never seen. 4. Digital-twin link: the model plugs into a digital twin of the asset so predictions carry physical context and simulation and forecasting stay in sync. Every model ships with EU AI Act-ready documentation, and you own the code and the pipeline.
The value comes from moving maintenance decisions along this ladder — from calendar to condition:
| Approach | How it decides | Trade-off |
|---|---|---|
| Reactive | Fix after failure | Cheapest to run, but the most unplanned downtime and collateral damage |
| Preventive | Fix on a fixed schedule | Predictable, but replaces healthy parts and still misses random failures |
| Predictive (AI) | Fix on measured condition + forecast | Highest uptime; needs sensor data and a maintained model |
Pricing is fixed and staged so you never buy the next phase before the last one pays off. A €2,500 audit maps your sensors, data quality and the failure modes worth targeting. A €20,000 proof of concept proves prediction on one asset class against real historical failures. Production starts from €50,000, deployed to your infrastructure with monitoring and retraining — and you own the code.
Audit (€2,500): two-to-three weeks scoping data readiness, sensor gaps and the highest-value failure modes, with a go/no-go recommendation. Proof of concept (€20,000): one asset class, real data, measured precision and lead time on held-out failures — proof before scale. Production (from €50,000): a hardened pipeline, monitoring, alerting into your CMMS / maintenance workflow, and scheduled retraining as machines and duty cycles change. We build, deploy and hand over — the code and models are yours.
Fixed and staged: €2,500 audit, €20,000 proof of concept, production from €50,000. The audit scopes your data and failure modes, the proof of concept predicts on one asset class, and production deploys to your own infrastructure. You own the code and can stop after any stage.
Enough labelled history to see failures repeat, not just occur. That usually means several months of time-series data plus your maintenance and breakdown records. The audit checks this first: if a failure mode has too few examples, we say so and add sensors or wait.
A digital twin is a physics- or data-driven model of the asset itself. We link the prediction model to it so forecasts carry physical context — load, wear, operating point — and so simulation and prediction share one representation of the machine.
Yes, and industrial maintenance normally sits in the lower-risk tiers. Every model ships with EU AI Act-ready documentation: intended use, data lineage, validation on held-out failures, and human oversight of maintenance decisions. You own the code, so you can evidence all of it yourself.
Yes. The same pipeline extends to camera-based inspection and defect detection. Vision models flag defects on the line while sensor models predict machine failure, and both feed the same data platform. Many manufacturers start with one and add the other later.
Book a €2,500 predictive maintenance audit and get a data-readiness verdict plus the failure modes worth targeting first — with no obligation to go further.
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