Product
Lean Physical AI
High-quality data infrastructure for autonomous vehicles, robotics, and physical AI systems.
Physical AI demands more than a labeling platform. It requires sensor fusion expertise, scenario-based curation, model evaluation against safety-critical edge cases, and a foundation model that adapts to your taxonomy without locking you in. Lean Physical AI delivers all of it, at the scale and quality frontier autonomy programs require.
500M+
Annotations processed monthly
3,800+
km of roads mapped weekly
10x
Faster labeling with AFM-1
5
CV tasks in one foundation model
Platform Capabilities
Built for the complexity of physical world AI.
Multi-Modal Sensor Annotation
Industry-leading annotation of 2D and 3D data from cameras, LiDAR, radar, and IMU sensors. ML-assisted labeling workflows with best-in-class interfaces for high-volume throughput.
Automotive Foundation Model
A single unified model trained on millions of densely labeled images across object detection, instance segmentation, semantic segmentation, panoptic segmentation, and classification tasks.
Data Exploration and Curation
Explore labeled and unlabeled data through natural language search. Understand dataset distribution, identify coverage gaps, and curate slices that match target operational scenarios.
Model Analysis and Evaluation
Analyze ML model performance at granular object classification levels. Explore model metrics, identify weaknesses, and run evaluation against targeted scenario test suites.
Scenario-Based Testing
Curate data by scenario type, object class, and edge case category. Run targeted evaluations against long-tail scenarios that matter most for safety validation.
Flexible Taxonomy Management
Iterate on data requirements without being locked into a fixed taxonomy. Add new object classes, adjust label hierarchies, and adapt to evolving model requirements without restarting pipelines.
3D Sensor Fusion
Fuse data from multiple sensor modalities into unified 3D scenes. Calibration, synchronization, and cross-modal annotation for full-stack perception model training.
Road and Environment Mapping
Map road networks, lane geometry, and static environment features at scale. High-frequency mapping data for HD map production and localization model training.
Robotics and Drone Support
Extend automotive-grade data infrastructure to manipulation robotics, UAVs, and industrial automation. The same data pipeline, applied to any physical AI system.
Lean AFM-1
Automotive Foundation Model, generation one.
Lean AFM-1 is a single unified perception model trained on millions of densely labeled images, covering object detection, instance segmentation, semantic segmentation, panoptic segmentation, and classification in one model. Iterate on your data taxonomy without retraining from scratch. 10x faster labeling on new data from day one.
Industries
Physical AI across every deployment context.
Autonomous Vehicles
End-to-end data infrastructure for AV development. From raw sensor data to evaluation-ready datasets, supporting every stage of the autonomy stack from perception to planning.
3,800+ km mapped weekly
Robotics
Training data for manipulation, navigation, and human-robot interaction. Annotation workflows adapted for robot-specific sensor configurations and task definitions.
Multi-modal sensor fusion
Drones and UAVs
Aerial imagery annotation, object detection for UAV applications, and scenario-based evaluation datasets for autonomous drone systems across defense and commercial use cases.
Aerial and satellite imagery
Industrial Automation
Visual quality inspection annotation, defect detection datasets, and manufacturing environment mapping for factory automation and industrial robotics deployments.
Real-time defect detection
Build the data stack your autonomy program needs.
Talk to our physical AI team about your sensor modalities, annotation requirements, and model evaluation needs.