Powered by Foundation Models. Driven by Data.

To achieve incredibly fast and reliable detection at an enterprise scale, you need lots of highly-specific training data. Off-the-shelf pre-trained models are great, but they aren't trained specifically on building facades, micro-stains, or complex architectural geometries.

By using SAM 3 as a zero-shot foundation model, we can accurately segment anomalies right out of the box. This allows us to enter a phase of continuous facade scanning immediately. As we fly and scan, we are building a proprietary, highly-accurate dataset of facade defects that simply does not exist anywhere else. Once we have enough data, we will train our own specialized edge models—securing an insurmountable data moat that competitors won't have.

You might notice that processing takes a few seconds per frame. SAM 3 is a massive, highly capable foundation model that almost perfectly replicates the output we would eventually get from a fully trained, highly-optimized model. However, because it's calculating zero-shot segmentations on the fly, it's computationally heavy.

We are trading initial inference speed during this Beta phase for maximum accuracy and data collection quality. Once we've amassed our proprietary dataset and trained our own lightweight models (like YOLOv10 or specialized edge architectures), the system will run entirely on-device, in real-time, at ultra-high framerates.

Right now, we are proving the concept: we can fly, we can detect, and we can map anomalies. The next phase is scale. By collecting millions of annotated facade images through our SAM 3 pipeline, we are actively training the intelligence that will power our autonomous fleet.

The drones won't just detect—they will eventually clean, paint, and 3D scan with pinpoint precision, knowing exactly what kind of surface they are looking at. The data we collect today is the intelligence that drives our automation tomorrow.