Enterprise AI data services

Bounding boxes, polygons, semantic and instance segmentation, keypoints, and object tracking with consistent IDs across frames. Deepen's in-house annotation team works in Deepen Annotate, where AI-assisted tools such as one-click segmentation and auto-labeling of common classes speed up the first pass, and people verify the labels. Output exports to ASAM OpenLABEL, COCO, KITTI, Pascal and other formats, or to your own schema.

3D bounding boxes, point-cloud semantic and instance segmentation, polylines and multi-frame tracking on LiDAR and radar data. Labels are made once across calibrated camera, LiDAR and radar, so each annotation projects into every sensor view. Scenario, behavior and intent labels add context for planning and prediction models. Run by Deepen’s LiDAR and multi-sensor specialists, with your taxonomy or a starting template.

Intrinsic, extrinsic and temporal calibration for camera, LiDAR, radar and IMU, solved in one system with target-based or targetless captures. Deepen's targetless method is patented. Choose the managed service, the Calibrate product your engineers run, or the self-serve calibration API for lidar-camera and multi-lidar extrinsics from a recording you already have.

For when the dataset you need does not exist yet. Deepen scopes the requirement, runs the collection, checks each batch against the agreed quality bar, and delivers in the schema and formats your pipeline reads. Modalities include images and video, point clouds and depth, synchronized camera, LiDAR, radar, IMU and GNSS streams, motion capture, egocentric and wearable capture, audio and text. Looking for data that already exists? Deepen Refinery is a data marketplace in invite-only beta.

Deduplication, scenario and edge-case selection, and long-tail mining across raw camera, LiDAR, radar, IMU and CAN logs, so you label the smaller set that improves the model. Curation works on embeddings computed from raw data, before any labels exist. The selected set comes back with the reason each frame was kept.

Scenario coverage scoring, label audits against your rules, and comparison of any two label sets, such as review against final, one vendor against another, or model output against verified data, scored on matching rate, category agreement and IoU. Results are tied to the frames they came from. Need an outside read on calibration integrity, label quality or coverage before you accept a delivery? Deepen also runs independent data assessments.

People judge what automated metrics cannot: whether a model made the right call in a real scene. Reviewers watch multi-camera video with audio and telemetry, write the situation and the goal, then check and correct the model's decision, action and post action in Deepen's VLA tool. They also review the frames Validate flags where model output and verified labels disagree.