What data annotation services do you offer?

We provide image annotation, video annotation, text and NLP labeling, 3D LiDAR cuboids, audio transcription, semantic segmentation, and keypoint datasets — all with human-in-the-loop quality assurance for production AI.

Who needs a data labeling company?

Machine learning teams building computer vision, autonomous vehicles, retail analytics, healthcare AI, security systems, or NLP products partner with us when they need scalable, accurate training data instead of in-house labeling bottlenecks.

How do you ensure annotation quality?

Every project uses written guidelines, consensus review, inter-annotator agreement checks, and multi-tier QA — targeting 99.5% accuracy for safety-critical and enterprise computer vision workloads.

Do you support GDPR and enterprise security?

Yes. We offer encrypted data pipelines, access controls, and GDPR-ready handling for EU and global enterprise customers shipping production ML models.

What industries do you annotate data for?

Retail, automotive and AV, healthcare, agriculture, sports analytics, security and surveillance, livestock monitoring, and worker safety — with domain-trained annotator pools.

Do you annotate LiDAR point clouds?

Yes. We provide 3D cuboids, polygon regions, and lane annotations for LiDAR and fused sensor datasets used in AV and robotics.

What QA do you use for autonomous vehicle data?

Multi-pass review, consensus scoring, and specialist QA for safety-critical classes with documented inter-annotator agreement targets.

Which export formats are supported?

COCO, KITTI-style layouts, and custom JSON schemas aligned to your training pipeline.

← All industries

Use case

LiDAR cuboids and perception labels for autonomous vehicles

Autonomous vehicle teams rely on precisely labeled LiDAR point clouds, camera frames, and fused sensor data. We support cuboid, lane, and traffic object labeling with workflows built for safety-critical ML.

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Industry challenges

!Complex urban scenes with pedestrians, cyclists, and weather
!Tight inter-annotator agreement on 3D cuboid edges
!Massive frame volumes across multi-camera rigs

How we help

LiDAR cuboids, 2D boxes, and lane polylines
Temporal tracking across frame sequences
Consensus review aligned to ISO-style QA expectations

Annotation types

3D cuboidsLane lines2D detectionSensor fusion

Automotive & AV data annotation services

Autonomous driving and ADAS perception require precise 3D and 2D labels across LiDAR, camera, and radar fusion stacks. Small cuboid errors compound into unsafe predictions — which is why safety-critical programs demand multi-tier human QA.

We annotate urban, highway, and parking scenarios with pedestrians, cyclists, vehicles, and static infrastructure. Our workflows support KITTI-style exports, custom JSON schemas, and frame-sequential tracking.

Partner with a data labeling company that understands AV timelines: we run 24/7 operations with SLAs aligned to your release trains.

Key benefits

✓LiDAR cuboids with edge-case review for partial occlusion
✓Lane lines, drivable area, and traffic object polylines
✓Temporal consistency across multi-camera sequences
✓Safety-aligned QA for production perception stacks

Best practices for automotive & av labeling

Lock cuboid conventions early (heading, size rules)
Run consensus on ambiguous distant objects
Validate exports against your simulator or training loader

Frequently asked questions

Do you annotate LiDAR point clouds?: Yes. We provide 3D cuboids, polygon regions, and lane annotations for LiDAR and fused sensor datasets used in AV and robotics.
What QA do you use for autonomous vehicle data?: Multi-pass review, consensus scoring, and specialist QA for safety-critical classes with documented inter-annotator agreement targets.
Which export formats are supported?: COCO, KITTI-style layouts, and custom JSON schemas aligned to your training pipeline.