Predictive Maintenance

Your machines are trying to tell you something.

Canari uses standard cameras and visual AI to detect equipment degradation, wear patterns, and anomalies before they become breakdowns. No proprietary sensors. No $50K retrofits.

Open Dashboard
$260K/hr
Average cost of unplanned downtime
30-50%
Downtime reduction with PdM
$91B
Market size by 2034

The industry runs on expensive guesswork.

Most predictive maintenance solutions require installing thousands of dollars in vibration sensors, acoustic monitors, and temperature probes on every machine. Each sensor needs wiring, calibration, and maintenance of its own. The very thing meant to prevent downtime creates new points of failure.

The real cost of "predictive"

IoT sensor installation per machine $5K - $50K
Sensor calibration & maintenance $2K - $8K/yr
Integration with existing systems $15K - $100K
Time to first prediction 3-9 months

Cameras see what sensors miss.

Visual AI analyzes what's already visible: rust forming, belts wearing, vibrations you can see but sensors haven't caught yet.

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No new hardware

Works with cameras your factory already has, or add inexpensive off-the-shelf cameras. No proprietary sensors, no vendor lock-in.

Minutes, not months

Point a camera at a machine. Canari learns what "normal" looks like in hours. Starts detecting anomalies immediately.

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Autonomous monitoring

24/7 visual analysis that never blinks. Detects micro-cracks, wear patterns, thermal anomalies, fluid leaks, and misalignments before humans notice.

Three steps. Zero sensors.

1

Connect cameras

Point standard IP cameras at your equipment. Canari integrates with any camera that outputs a video feed.

2

Learn the baseline

The AI watches normal operation and builds a visual model of healthy machine behavior. No manual labeling required.

3

Predict and alert

When visual patterns deviate from normal, Canari alerts your maintenance team with what's degrading, how fast, and when to act.

The canary that never stops watching.

Built by people who spent years teaching AI to see defects at 0.1mm accuracy in manufacturing. Now we're turning those same eyes on the machines themselves. Because every factory deserves an early warning system that costs less than the problem it prevents.