Utilities spend serious money flying LiDAR over their corridors — then wait weeks for processing, receive a folder of files, open it twice, and archive it. We think the survey should come back as a living system. Here's what that looks like, with real numbers.
Every grid operator knows the cycle. You commission a corridor flight. The point cloud lands at a processing vendor. Weeks pass. What comes back is a deliverable in the most literal sense: files. Beautiful, expensive, cm-precise files — that start aging the moment they arrive.
The geometry was current on the day of the flight. The vegetation kept growing. The work orders were extracted once, by hand, by an analyst reading a PDF. And the next useful event is… next year's flight.
The problem was never the LiDAR. The problem is that the output is treated as a document instead of a system.
Here's a run on our platform with a sample drone-captured corridor file, 14.24 million points over 0.518 km². Classified, vectorized, and packaged into deliverables in 2 minutes 52 seconds, on commodity hardware (about 10.8 km² per hour).
The classification runs on our own, purpose-trained neural networks — the same BioGuardNet-H pipeline that powers our satellite products. You ship a LAS, LAZ or COPC file from any source — drone, helicopter, vehicle, terrestrial. No preprocessing on your side.
Those four files aren't the product. They're how the system talks to the tools you already run. What you actually get is a continuous platform: a live 3D control surface you can walk through in the browser, automated risk monitoring, alerts when vegetation crosses clearance thresholds — and a direct API into your maintenance workflow. CMMS, work order management, GIS, SCADA dashboards, ERP. No custom adapters, no analyst-hours spent reformatting.
Inside the same 3D view, your operators can measure clearances, design corridors (place towers, draw conductors, validate against the cm-precise cloud — no CAD round-trip), and generate a one-click AI diagnostic report: class distribution, vegetation risk per height class, a combined 0–100 risk score with forecast, exported as an audit-ready PDF.
LiDAR is not a fault-detection tool. It doesn't read serial numbers off insulators, it doesn't see corona discharge, it doesn't replace a thermal camera.
What it does — extremely well — is geometry: exactly where every wire is, exactly where every tree is, and exactly how close the two are to crossing safety thresholds. We do the geometry layer. Other inspections complement it. We integrate with all of them.
One sensor isn't enough, and pretending otherwise is how budgets get wasted. The LiDAR captures structure with centimetre precision — but you fly it once a year. The satellite watches every span every 5 days — but can't measure exact distances. Fused, they tell the full story:
For every span in the network, the satellite NDVI trend predicts growth, the LiDAR validates current clearance, and the model fuses both into a forward-looking risk score — span by span, out to 2030. Not a report. A live diagnostic surface across your entire network.
Our industry loves "contact us for a quote". We don't. The price is on the website, and it's one number:
Storage, AI classification, dashboard hosting, clearance reports, work orders, the Sentinel-2 change overlay, the NIS2-compliant audit log — included. All of it runs on EU-resident infrastructure, in customer-isolated tiers, with processing on ephemeral GPU compute that purges your files the moment inference ends. No infrastructure investment on your side. No offshore data egress. No surprises in procurement.
We publish the number because the economics should be checkable on a napkin — by you, before the first call.
A sample corridor segment from public national LiDAR — 577,887 AI-classified points — rendered live in your browser. Drag to rotate, scroll to zoom. Then talk to our engineering team about your network.