Deepen AI
Deepen AI provides a verified data infrastructure stack for Physical AI systems, combining multi-sensor calibration, data curation, 3D annotation, and validation into a single certified platform. The stack supports autonomous vehicles, robotics, and industrial applications with traceable, auditable data pipelines that meet safety standards like ISO 27001, SOC 2 Type II, and EU AI Act readiness. Deepen reports 98.6% first-pass annotation accuracy and over 200 calibrated sensor configurations.
- Artificial Intelligence
- Autonomous Vehicles
- Automotive Technology
- Robotics
- Mobility & Transportation
- Software Only
- Data & Analytics
- Enterprise Software
Funding
Funding not disclosed
Founders
Product
Problem
Physical AI teams—building autonomous vehicles, robots, and industrial systems—spend 60-80% of their data engineering time on data preparation rather than model development. Uncalibrated sensors, mislabeled edge cases, unverified coverage, and missing provenance documentation cause perception models that ace benchmarks to fail in real-world conditions, creating safety risks and regulatory compliance gaps.
Solution
Deepen AI provides a single, connected data infrastructure stack that brings calibration, capture, curation, annotation, and validation into one platform. The system self-calibrates camera, LiDAR, radar, and IMU sensors to sub-pixel accuracy without checkerboard targets, then applies AI-assisted 2D and 3D annotation with automated quality checks and human verification. Every step—from raw sensor data to training-ready labels—is traceable through label, score, and audit, enabling teams to meet industry safety standards and pass regulatory scrutiny. The platform is neutral by design, holds no competing models, and offers flexible deployment on-premise or in the cloud.
Target Audience
Deepen AI serves enterprises and startups developing autonomous systems across automotive (L2-L4 AV and ADAS programs), robotics and embodied AI (manipulation, navigation, warehouse autonomy, humanoids), and energy/industrial sectors (inspection, digital twins, mapping, monitoring).
Features
- Patented self-calibrating multi-sensor calibration covering camera-LiDAR, LiDAR-to-LiDAR, IMU-to-LiDAR, radar-to-camera, stereo and intrinsic pairings, achieving sub-pixel reprojection error and sub-1° angular extrinsic rotation
- AI-powered 3D point cloud annotation with one-click bounding boxes, semantic segmentation, temporal consistency across sensor sequences, and inter-annotator agreement scoring
- Data curation engine performing deduplication, scenario selection, and long-tail mining to surface the most valuable frames for labeling
- Auto Quality Checker that identifies labeling issues automatically, with 98.6% first-pass accuracy and 99.5% full-QA accuracy reported
- Built-in provenance documentation creating an immutable record from sensor collection to training loop, supporting EU AI Act Article 10 compliance
- Independently auditable certifications including ISO 27001, SOC 2 Type II, TISAX, GDPR, ASPICE-ready, UNECE R155, and Euro NCAP 2026 readiness
- ASAM OpenLABEL Author compliance and Safety Pool™ integration for shared driving scenarios and safety data initiatives
- Fleet-ready calibration with zero downtime, enabling in-field recalibration without pulling vehicles back to a bay
- Customizable data export, real-time productivity analytics, and built-in QA workflows with collaborative feedback loops