We produce high-quality labeled images, video datasets, 3D assets, and specialized data pairs that power the next generation of computer vision and multimodal AI.
Start a ProjectPixel-perfect annotations, bounding boxes, segmentation masks, and keypoint labeling for object detection, classification, and scene understanding models.
Frame-by-frame annotations, temporal tracking, action recognition labels, and event detection data for video understanding AI.
Synthetic 3D scenes, depth maps, point cloud annotations, and multi-view datasets for spatial AI and robotics applications.
Curated transformation datasets showing state changes, edits, and modifications for training image-to-image and editing models.
Screen recordings, UI interaction traces, and desktop navigation datasets for training AI agents that operate computer interfaces.
Bespoke data collection and annotation pipelines tailored to your unique model requirements, edge cases, and domain specifics.
Multi-sensor dataset combining LiDAR point clouds, camera feeds, and semantic segmentation for a leading autonomous vehicle company's perception stack.
High-quality before/after image pairs spanning exposure correction, color grading, object removal, and style transfer for training diffusion-based editing models.
Comprehensive dataset of human-computer interactions including mouse movements, keystrokes, and screen states for training autonomous computer-using agents.
Founded by practitioners who've built production ML systems and understand what models actually need.
We believe the quality of AI systems is fundamentally limited by the quality of their training data.
Most data providers optimize for volume. We optimize for signal. Every dataset we produce is designed to maximize information density and minimize annotation artifacts that confuse models.
Our team combines deep ML engineering experience with rigorous annotation methodology. We don't just label data—we partner with research teams to understand model failure modes and design targeted data interventions.