Every data type. Every modality.
Six annotation pipelines, one platform. Mix and match across your training data stack — we scale each independently.
Image Segmentation & Bounding Boxes
Pixel-precise masks and tight bounding boxes for object detection. Instance segmentation at 99.2% IoU across COCO-style datasets.
NER & Text Annotation
Named entity recognition by domain-expert annotators. Oncology, legal, finance — we staff labelers who understand your terminology.
LiDAR Point Cloud Annotation
3D cuboid annotation for autonomous vehicle stacks. Certified for Waymo Open Dataset format. Average 38-minute QA turnaround.
RLHF Preference Ranking
Human preference data for LLM fine-tuning. Structured ranking tasks with inter-annotator agreement tracked per batch. Built for Constitutional AI workflows.
Video Frame Annotation
Frame-by-frame object tracking, action recognition labeling, and temporal segmentation. Supports CVAT, Labelbox, and Scale AI export formats.
Audio & Speech Annotation
Transcription, speaker diarization, intent labeling, and sentiment tags. Supports 42 languages. Medical and legal audio handled by certified specialists.
Numbers that hold up in production.
Every batch ships with a consensus report. Disagreements above threshold are automatically routed to a senior reviewer before delivery — not after.
Fleiss κ across all task types
Before rework cycles
Ambiguous samples flagged correctly
Parallel Annotation
Each item labeled independently by 3+ annotators. No collaboration during initial pass.
Consensus Engine
Automated agreement scoring. Items below κ threshold flagged for review.
Senior QA Review
Domain expert resolves flagged items. Audit trail attached to every delivery.
Gold Standard Test
Random 2% sample validated against your gold set. Score reported in delivery receipt.
We were stuck at 40K labeled frames per week internally. Label.ai scaled us to 300K in the first month. Our model validation cycle dropped from 6 weeks to 11 days.
Priya Raghunathan
ML Infrastructure Lead · Luminary Robotics
The NER team understood oncology terminology without a lengthy onboarding. They labeled 2.4M clinical notes with 98.9% agreement against our gold standard.
Dr. Marcus Webb
Head of NLP Research · Nexagen Therapeutics
We gave them our RLHF spec on a Monday. By Thursday we had 50K ranked preference pairs, formatted exactly for our Constitutional AI training loop.
Soo-Jin Park
Foundation Model Engineer · Argent AI
Know your number before you commit.
Configure your annotation job and get a per-unit price range instantly. No sales call required until you're ready.
Per-unit estimate
per annotated unit
Monthly total range
Get a Pilot Batch Free
Send us 500 raw samples. We'll annotate them, deliver a consensus report, and you decide if we're the right fit. No contract, no credit card.