Data products across input types
Audio, image, video, LiDAR and sensor, and text and evaluation data, captured first-party under controlled
variability with verified consent under GDPR Article 7 and the provenance record EU AI Act Article 10 expects.
Speech captured where systems fail
Multi-dialect speech recorded in the conditions that break production models: in-vehicle noise, reverberant rooms, far-field pickup, accented and emotional speech. Captured at 48 kHz / 24-bit across 50+ dialects and 150+ languages, 100% human-reviewed.
- In-vehicle and far-field capture
- Multi-accent, multi-dialect pools
- Emotion and speaking-style variation
- Parallel corpus and MTPE, 38+ language pairs
48 kHz / 24-bit, 150+ languages
First-party video for motion and time
Video captured first-party for tasks that depend on motion and sequence: driver monitoring, gesture and body-pose, action over time, and multi-camera synchronization with documented scene variability.
- Driver monitoring and gaze
- Gesture and body-pose over time
- Multi-camera synchronization
- Documented lighting and occlusion
Proprietary capture platform
Still-frame capture across real variation
First-party image capture spanning lighting, viewpoint, device, and demographic variation, delivered with bounding-box, segmentation, keypoint, and polygon labels at the schema your model expects.
- Lighting, viewpoint and demographic spread
- Bounding-box and polygon labels
- Semantic and instance segmentation
- Keypoint and landmark annotation
Capture plus pixel-level labels
Point clouds and fused sensor data
LiDAR, radar, and multi-sensor data with calibration-verified alignment for ADAS, robotics, and spatial AI: 3D cuboids, per-point segmentation, and time-synced sensor fusion.
- 3D cuboid and per-point segmentation
- Calibration-verified sensor fusion
- LiDAR, radar, IoT and wearables
- Time-synced multi-sensor capture
Spatial and sensor-fusion ready
Language data and model evaluation
Text and parallel-corpus data across 38+ language pairs, plus the evaluation sets that catch failure before deployment: preference data, red-team prompts, and human grading by native speakers.
- Parallel corpus and MTPE, 38+ pairs
- Preference and ranking data
- Red-team and safety evaluation
- Native-speaker human grading
150+ languages, human-graded