Image & Video Annotation
Bounding boxes, polygons, semantic segmentation, keypoints and instance masks — for computer vision pipelines that ship to production.
bbox · polygon · segmentationAI / ML · Data Annotation
Labels your model can trust.
Precise, scalable annotation across image, video, text, audio and 3D — with QA pipelines that catch errors before your training run does.
50M+Labels delivered
< 2%Avg. error rate
48 hrPilot turnaround
Annotation pipeline · live
Image segmentation94%
NER tagging78%
RLHF preference61%
Audio transcription88%
IAA 0.91Gold-set ✓QA gated
What you get
Six annotation capabilities, one quality standard.
From pixels to paragraphs — every modality your training pipeline needs.
Text & NLP Labeling
Named entity recognition, sentiment tagging, intent classification and relation extraction with domain-expert annotators.
NER · intent · relationAudio Transcription
Speech-to-text, speaker diarization, accent and dialect coverage at scale — trained annotators, not generic ASR output.
ASR · diarization · multilingualRLHF & Preference Data
Ranked outputs, pairwise comparisons and reward-model training sets built by expert raters who understand your domain.
RLHF · preference · ranking3D LiDAR & Point Cloud
Cuboid annotation, point-level segmentation and sensor-fusion labeling for autonomous systems and robotics.
LiDAR · cuboid · fusionQuality & Consensus
Inter-annotator agreement, gold-set injection, IAA scoring and disagreement resolution — so your labels are as clean as your model deserves.
IAA · gold-set · QAHow it works
Pilot in 48 hours. Scale in a week.
Quality gates at every stage — not just at delivery.
01
Day 1–2Taxonomy design
Define label taxonomy, edge cases and annotation guidelines with your ML team.
02
Day 3–5Pilot batch
Annotate 500–2k samples. Measure IAA. Revise guidelines before scaling.
03
Week 2–3Scale with QA
Full throughput with gold-set injection every 500 tasks to catch drift.
04
Week 4+Active learning loop
Model flags uncertain samples. Annotators focus effort where it matters most.
05
OngoingContinuous delivery
Weekly batches, drift reports and label-schema versioning as your model evolves.
Tech stack
Tools we run. Platforms we support.
Annotation tools
Label StudioCVATScale AIArgillaProdigy
Computer vision
RoboflowSuperviselyV7 DarwinHasty.ai
NLP & text
DoccanoLabelBoxspaCySnorkelCleanlab
QA & pipeline
Gold-set injectionIAA scoringKrippendorff αActive learningConsensus voting
Where it pays back
Three annotation programs that moved the model.
Real throughput, real IAA, real downstream model improvements — not demo datasets.
1.2M frames annotated in six weeks.
Polygon + cuboid pipeline for a Tier-1 AV client. Multi-class semantic segmentation with < 2% error rate measured against gold set.
< 2%Error rate
1.2MFrames
CVATLabel StudioPython QA
Preference data that cut hallucinations 38%.
Domain-expert raters scoring 80k response pairs across medical Q&A. Delivered reward-model training set with 0.91 Krippendorff's alpha.
−38%Hallucinations
0.91αIAA score
ArgillaCustom rubricExpert raters
Custom NER for 14 entity types.
Legal document labeling — contracts, clauses, obligations, parties. 200k spans tagged with active-learning loops to cut annotation cost 40%.
−40%Annotation cost
0.94 F1Final model
ProdigyspaCyActive learning
Why ETY
Annotators who understand ML, not just clicks.
50M+Labels delivered across vision, NLP and audio pipelines.
< 2%Average error rate measured against gold-set benchmarks.
0.91αMedian Krippendorff's alpha across multi-annotator tasks.
48 hrPilot batch turnaround — taxonomy to first labeled samples.
Send us your worst labels.
Share your annotation task — or the dataset you're not proud of. We'll come back with a QA audit, a label schema and a pilot plan inside 48 hours.