Labels your autonomy stack can depend on.
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.
For the teams building real-world autonomy.
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.
What we bring to the table.
Perception annotation
Pixel-accurate 2D and 3D annotation across camera, LiDAR, radar, and fused sensor streams. Built for perception teams training production autonomy stacks.
2D bounding boxes
Tight boxes with class, attribute, and occlusion labels.
Semantic segmentation
Pixel-level class assignment across driving scenes, indoor, industrial.
3D cuboids
Oriented 3D bounding boxes in LiDAR/point-cloud with orientation, velocity.
Lane & freespace
Drivable-area segmentation, lane-line annotation, barrier identification.
Temporal & tracking
Object tracking, trajectory labeling, and temporal event annotation for video and sensor sequences. Consistent identity through occlusions and scene changes.
Object tracking
Persistent IDs across frames with re-identification through occlusion.
Trajectory labeling
Full motion paths with velocity, heading, and predicted intent.
Event annotation
Temporal action localization — merging, braking, crossing, loitering.
Keyframe interpolation
Efficient temporal labeling with dense ground-truth at keyframes.
Scenario mining & labeling
Targeted collection and annotation of rare, safety-critical scenarios. The long tail your fleet rarely sees but your model absolutely must handle.
Rare-event mining
Automated candidate surfacing from fleet data, reviewed by experts.
Safety-critical scenes
Pedestrians at dusk, construction zones, emergency vehicles, near-misses.
Weather & edge conditions
Rain, snow, glare, night, sensor-degraded conditions labeled and categorized.
Regional driving culture
Local-driver behavior labeling across 30+ geographic markets.
Robotics & industrial
Beyond self-driving: warehouse robotics, drones, agricultural autonomy, and industrial inspection. Domain-tuned annotation for non-automotive autonomy.
Manipulation labeling
Grasp points, affordance regions, object-part segmentation.
Indoor navigation
Floor-plan mapping, obstacle classification, semantic scene labels.
Aerial / drone
Overhead scene annotation, flight-path obstacle identification.
Anomaly detection
Defect classification for industrial inspection and quality control.
Where autonomy teams use us.
Passenger AVs
Full perception stack training for L2–L4 autonomous driving programs.
Commercial trucking
Long-haul and last-mile freight autonomy with highway and urban coverage.
Warehouse robotics
Inventory, picking, navigation, and safety labeling for fulfillment automation.
Agricultural autonomy
Crop, weed, livestock annotation for precision farming platforms.
Defense & security
Perimeter, threat, and asset labeling with appropriate clearance programs.
Industrial inspection
Defect, wear, and anomaly annotation for visual QA systems.
Common questions.
How do you handle safety-critical quality?
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.
Can you handle our custom taxonomy?
Yes. We've onboarded to 60+ custom taxonomies. Training takes 3–5 days per annotator batch; gold-set calibration continues throughout the engagement.
What sensor modalities do you support?
Camera (mono/stereo), LiDAR, radar, ultrasonic, IMU, GPS — and fused representations. Point-cloud formats include PCD, LAS, and custom proprietary.
How fast can you ramp up?
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.
Can you work in our own tooling?
We work in Scale Nucleus, Deepen.AI, Labelbox, CVAT, or your internal tooling — or ours. Tooling choice is never the bottleneck.
Let's make your AI better together.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.