Detection got cheap.
Closure didn’t.
In five years the plant floor filled with ways to find a leak: acoustic cameras, inspection robots, dashboards that rank findings by severity. The leaks kept leaking. A found leak and a fixed leak are different things, and almost everything on the market stops at the first one.
What it leaves behind is a graveyard of open tickets. Hundreds of findings, none located precisely enough for a technician to walk to with a wrench, and no record of which were ever actually fixed. Your team spends its hours hunting. Your CFO has no proof the maintenance spend stopped the bleed.
Drag to re-scan →3 sources open
Pl. 2 — same pose, same frequency bin, before and after repair. Synthetic illustration.
Three things that have to be true before a leak is worth finding.
The robot is the instrument
Movement isn’t travel between measurements, it is the measurement. Bearings taken from several angles intersect against a LiDAR map, which turns an acoustic direction into a 3D coordinate someone can walk to with a wrench.
Work orders, not dashboards
A confirmed source should leave as a targeted job in the CMMS you already run, over the MQTT and OPC UA your floor already speaks, carrying its 3D location, its acoustic evidence and its estimated annual cost. Not one more dashboard for somebody to stop watching.
Verification is the deliverable
After the repair the robot returns to the same pose and scans the same frequency bin. The job closes when the acoustic signature is gone, and not before. That verified record — not the heatmap — is the product.
Everything before the last step is inspection.
- Detect
- Locate in 3D
- Work order
- Repair
- Re-scan
- Verified
Only the last step is recovery. A plant doesn’t want to know where its air is going; it wants to show that the spend stopped the bleed. That is a documentary problem as much as an acoustic one, and it is the part the category keeps skipping.
Where this stands today: detection and beamforming run on our own bench. Multi-pose localisation, CMMS integration and the verification record are in development and have not been validated on a production floor.
This is the part everyone else sells.
Every acoustic camera datasheet brags about the same thing: microphones in a spiral array, an FPGA running real-time delay-and-sum beamforming, a picture of where the noise is. Ours is thirty MEMS channels on an Artix-7 at a 20 kHz bin, bench-built from an open-source design for a fraction of what a boxed camera costs.
We built it because the metrology engine needs raw acoustic data at full rate, not a vendor’s rendered image. But the sensing layer is modular and sensor-agnostic by design: our array or a commercial camera, the intelligence sits on top either way. That is why we don’t describe ourselves as a camera.
- Channels
- 30
- Aperture
- 90 mm
- Target bin
- 20 kHz
- Sample rate
- 62.5 kHz
- Frame rate
- ≈60 fps
- Angular resolution
- ≈11° estimate, λ/D
- Status
- Bench prototype
Stop finding leaks.
Start proving closure.
We’re early, and we’re looking for pilot sites: plants willing to run closed-loop verification alongside the inspection they already do. If that could be yours — or if you just want to tell us how inspection really works on your floor — we want to hear from you.