Human-verified labeling for training-ready datasets.
3-step QA (annotator → reviewer → founder) and a <1% error guarantee. COCO, YOLO, Pascal VOC and custom JSON formats supported.
What you get
- Clear guidelines & edge-case playbooks
- Consistent naming, classes & versions
- Weekly QA snapshots & samples
Bounding Boxes
Object detection with box IoU targets and class consistency rules. Multi-object scenes, small objects, and occlusions handled.
- COCO YOLO txt/json
- Class maps & examples
- Hard cases flagged
Polygon Segmentation
Pixel-precise masks for irregular shapes. Rules for holes, overlaps and boundaries.
- COCO masks/RLE
- Boundary tolerances
- Visual QA overlays
Keypoints / Landmarks
Pose, facial landmarks or custom points with visibility and skeleton rules.
- Custom skeletons
- Visibility tags
- Consistency checks
OCR / Text
Transcription + boxes/polygons; language tags; redaction on request.
- Reading order
- UTF-8 clean
- Export CSV/JSON
Audio Labeling
Speech / speaker turns / intent tags. Time-aligned segments and confidence notes.
- VAD & diarization
- Multilingual
- JSON + timestamps
Dataset QA
Audits of third-party labels. Error heatmaps, confusion by class, and relabel plan.
- Sample reviews
- Error budget <1%
- Fix report
Our process
- Brief & class map alignment
- 10–30 sample pilot with QA
- Production with 2-person review
- Founder final QA & delivery
Deliverables
- Annotations in COCO / YOLO / VOC / custom JSON
- Guideline PDF + edge-case gallery
- QA summary (<1% error) + sample images
Retail & Logistics
Inventory, shelf detection, barcode/OCR.
Mobility & Drones
Traffic, pedestrians, road signs, aerial imagery.
Healthcare (non-PHI)
Instruments, anatomy outlines, posture.