The verified data stack for autonomous driving.
Fused multi-sensor data for L2 to L4 AV and ADAS programs: calibrated, labeled, and validated to the standards your regulators actually read. The data behind perception you can ship and certify.
Models that ace the benchmark still fail on the road.
Uncalibrated sensors, mislabeled edge cases, and unverified coverage are why a perception model that scores well in the lab misses the pedestrian at dusk. In automotive the gap between benchmark and behavior is measured in lives, recalls, and homologation delays. Deepen closes it before training ever starts.
Fusion only holds if calibration does.
When camera, LiDAR, and radar fall out of alignment, fused detections wander and the model learns from data that no longer matches the road.
The long tail decides whether you ship.
Occlusions, night scenes, rare maneuvers, and adverse weather are exactly the frames a benchmark under-samples and a regulator asks about.
You cannot certify what you cannot trace.
Without scenario coverage and an audit trail, 'the model is safe' is a claim. Homologation now demands it be evidence.
What we deliver for AV teams.
One verified stack across the sensors and standards an autonomous driving program runs on, from raw capture to a result you can certify.
Sub-pixel multi-sensor calibration
Self-calibrating camera, LiDAR, radar, and IMU locked to under 0.8px, so fusion stays true across the whole fleet with no checkerboard targets.
Production-scale 3D and 2D labeling
ASAM OpenLABEL 3D and 2D annotation on fused sensor data, AI-assisted and human-verified at 98.6% first pass, never crowdsourced.
Scenario coverage and audit trail
Coverage scoring across 300K+ validation scenarios with an immutable record, built for homologation under the EU AI Act and Euro NCAP 2026.
Clean capture and the data worth labeling
Hardware-synchronized capture with provenance from frame zero, then deduplication and long-tail mining that surface the scenarios worth labeling.
One chain, sensor to sign-off.
Every step carries forward, so the data you train and certify on traces all the way back to the sensor.
Patented self-calibration locks camera, LiDAR, radar, and IMU to sub-pixel accuracy, so multi-sensor fusion stays true across the fleet.
ASAM OpenLABEL 3D and 2D labeling on fused data, AI-assisted and human-verified at 98.6% first pass and 99.5% after full QA.
Scenario coverage and inter-annotator scoring across 300K+ validation scenarios, with Safety Pool™ coverage on every delivered set.
An immutable chain from raw signal to delivery. When a regulator asks how you know, you have the receipt, ready for homologation review.
Proven in the world's hardest driving programs.
Sensors, standards, and accuracy.
Which sensors does Deepen AI support for AV and ADAS programs?+
How is calibration accuracy measured?+
Which standards and homologation frameworks are covered?+
How do provenance and audit trails work?+
What labeling accuracy can I expect?+
See verified results on your sensor data.
Tell us the sensors you run and the standards you ship to. We will show you the verified stack behind your autonomous driving program.