Predictive maintenance
Vibration, temperature and current data turned into early warnings and work orders before a machine fails.
01 · Build
What we build
A bearing gives weeks of warning before it fails, if someone is listening.
- Sensor retrofits and capture for vibration, temperature and motor current
- Condition indicators per asset: RMS, kurtosis and bearing fault frequencies
- Anomaly and remaining-useful-life models trained on your own history
- Alerts that open work orders in your CMMS or ERP, with the evidence attached
- Drift checks, so warnings stay accurate as machines age
- Every prediction recorded against what was found, to measure accuracy
02 · Demo · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
- Assets watched48
- Open warnings3
- P-07 bearing life19 days
- Hit rate (90 d)86 %
03 · Features
What it does well
- 01
Per-asset baselines
Each machine is compared with its own normal, not a generic threshold for its type.
- 02
Explained alerts
A warning shows which indicator moved, by how much, and the trend that led to it.
- 03
Edge capture
High-rate vibration is reduced to features at the machine, so only results cross the network.
- 04
Work orders, not emails
Warnings land in the maintenance system your technicians already use.
- 05
Measured accuracy
Hits, misses and false alarms are counted against inspection results every month.
- 06
Planned stops
Remaining-life estimates are shown against the production plan to pick a repair window.
04 · FAQ
Questions
Do we need years of failure data?
No. Anomaly models learn from normal running and flag what departs from it, which works from the first few weeks. Remaining-life estimates need some failure history; where it is thin, we start with condition indicators and add those models as records build up.
Which machines are worth monitoring first?
The ones where a failure stops the line, costs the most to repair, or has a long lead time on parts. We rank assets with your maintenance team on those terms and start with a small set, so the value is visible before the programme grows.
How do you know the predictions are right?
Each warning is closed with what the technician found: a real fault, an early sign, or nothing. Those outcomes are counted every month, per asset type, and thresholds are tuned from them. You see the hit rate and the false-alarm rate, not a promise.
05 · Services
Services that go with it
- ServiceCustom software developmentSoftware designed and built around the way your business works, shown to you working at every review.
- ServiceAI integrationLanguage and vision models built into your existing software and workflows, with guardrails and measured accuracy.
- ServiceData engineeringPipelines and warehouses that turn data scattered across your systems into clean, trusted tables.
06 · Software
Related software
- SoftwarePredictive analyticsForecasts and risk scores built from your own history and shown inside the tools where decisions are made.
- SoftwareManufacturing softwareShop-floor systems that connect machines, work orders, quality checks and stock to show production as it happens.
- SoftwareEdge AIModels that run beside the machine or camera, measured on the device, with no round trip to the cloud.
- SoftwareIoT platformsConnected devices onboarded securely, monitored live and updated remotely from one platform.