For teams building off-road robots and vehicles
We find where the camera AI of your off-road machine fails, find out why — and fix it with the right data.
What the AI thinks each pixel is: trail grass trees bushes sky puddles person
Same AI, same trails
64%
worse at reading the scene at night, with only its headlights.
On this very frame it finds only 9% of the person on the trail — 93% on a clear day.
Averaged over 300 real trail frames. How we measure →
Built for
We put your model through dust, night, fog, rain and mud — three strengths each, every pixel still correctly labelled.
Not just a score: which objects it loses, in which conditions, and what in its training data causes it.
Real data for the gaps it has never seen, simulation only where the real thing can’t be captured — then retraining.
Before and after on your own real frames, against a control run — so you see the gain that survives the field.
Watch the demo · 40 seconds
Every number in the video is the AI’s actual output. Frames: GOOSE dataset, Fraunhofer IOSB (CC BY-SA 4.0).
What a fix looks like · a real case
A model trained on open fields — and trained with plenty of simulated bad weather — called 80% of the tree canopy on this forest road “sky”. Simulated weather didn’t fix that. What fixed it was the kind of real data it had never seen: roads under trees. Same frame afterwards: 0%.
Plans
Individual
Students, researchers, solo builders
Team
Startups and small robotics teams
Recommended
Pro
Teams shipping a product
Enterprise
Fleets, several models or cameras
No hidden costs: every audit ends with a fixed price for the fix, before you commit. Compare plans →
No. Simulated weather is one tool, and we measure it honestly: in our own tests it helps against simulated conditions, but on real bad weather the big gains came from the right real data. So we test first, find out what your model is missing, and use whichever fix actually moves your real-world numbers.
No. With the $19 stress-test pack you run everything yourself — nothing leaves your machine. For an audit we can work either way: you send the model, or we send you the test set and a script and you send back the results. An NDA is available before anything is shared.
Yes. A few photos of where your vehicle works are enough to start. Often the fastest win is openly licensed real data that already covers what you’re missing — we find it and map it to your classes.
The dust, night, fog, rain and mud are simulated with physics-based models on real photos, so every pixel keeps its correct label. The pack also includes 60 frames of real rain and snow, and we always check findings on real frames — our own biggest finding came from exactly that.
Individual: $19 for the stress-test pack. Team: audits from $300. Pro: audit + fix from $600 per model. Enterprise: custom. The fix is always priced before you commit.
From an audit: a failure map — how much worse your model reads the scene in each condition, which objects it loses first, and why. From a fix: a new version of your own model that handles those conditions better, the data that taught it, and a before/after report on your own real frames.
No drip campaign. One email when something new is measured.