Pixel-accurate segmentation, 3D bounding boxes, LiDAR annotation, and trajectory labeling — built by specialists trained on your taxonomy, not on whatever a gig worker thinks an oncoming vehicle looks like.
Autonomy data isn't labeling — it's safety engineering. A mislabeled pedestrian, a missed traffic cone, a confused object-permanence call — these propagate into the models that drive cars, fly drones, and move warehouse robots.
Our autonomy annotators are specialists. They train on your taxonomy, pass domain-specific gold sets, and work under multi-pass QA with safety-critical review gates. Nothing ships without being seen by at least three pairs of eyes.
Pixel-accurate 2D and 3D annotation across camera, LiDAR, radar, and fused sensor streams. Built for perception teams training production autonomy stacks.
Tight boxes with class, attribute, and occlusion labels.
Pixel-level class assignment across driving scenes, indoor, industrial.
Oriented 3D bounding boxes in LiDAR/point-cloud with orientation, velocity.
Drivable-area segmentation, lane-line annotation, barrier identification.
Object tracking, trajectory labeling, and temporal event annotation for video and sensor sequences. Consistent identity through occlusions and scene changes.
Persistent IDs across frames with re-identification through occlusion.
Full motion paths with velocity, heading, and predicted intent.
Temporal action localization — merging, braking, crossing, loitering.
Efficient temporal labeling with dense ground-truth at keyframes.
Targeted collection and annotation of rare, safety-critical scenarios. The long tail your fleet rarely sees but your model absolutely must handle.
Automated candidate surfacing from fleet data, reviewed by experts.
Pedestrians at dusk, construction zones, emergency vehicles, near-misses.
Rain, snow, glare, night, sensor-degraded conditions labeled and categorized.
Local-driver behavior labeling across 30+ geographic markets.
Beyond self-driving: warehouse robotics, drones, agricultural autonomy, and industrial inspection. Domain-tuned annotation for non-automotive autonomy.
Grasp points, affordance regions, object-part segmentation.
Floor-plan mapping, obstacle classification, semantic scene labels.
Overhead scene annotation, flight-path obstacle identification.
Defect classification for industrial inspection and quality control.
Full perception stack training for L2–L4 autonomous driving programs.
Long-haul and last-mile freight autonomy with highway and urban coverage.
Inventory, picking, navigation, and safety labeling for fulfillment automation.
Crop, weed, livestock annotation for precision farming platforms.
Perimeter, threat, and asset labeling with appropriate clearance programs.
Defect, wear, and anomaly annotation for visual QA systems.
Every safety-critical label goes through at least three reviewers, plus automated consistency checks. Disagreements escalate to senior annotators who hold a domain certification on your taxonomy.
Yes. We've onboarded to 60+ custom taxonomies. Training takes 3–5 days per annotator batch; gold-set calibration continues throughout the engagement.
Camera (mono/stereo), LiDAR, radar, ultrasonic, IMU, GPS — and fused representations. Point-cloud formats include PCD, LAS, and custom proprietary.
5 business days to stand up a 50-annotator team on a new taxonomy. Critical programs have been spun up in 72 hours with existing trained teams.
We work in Scale Nucleus, Deepen.AI, Labelbox, CVAT, or your internal tooling — or ours. Tooling choice is never the bottleneck.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.