01
Share Your Data
Upload raw images, video, text, audio, or LiDAR securely — we ingest from cloud storage, SFTP, or your existing ML pipeline.
Polygon and instance annotation for irregular regions — shelves, lanes, anatomy, and industrial boundaries with enterprise QA.
When objects lack clean rectangular bounds, polygon annotation captures true geometry for segmentation and instance models. We label shelf planes, lane boundaries, roof footprints, tissue regions, and industrial defects with written playbooks for edge ambiguity.
Rules for partial objects, shared boundaries, anti-aliasing, and zoom levels so polygon vertices stay consistent across annotators and capture conditions.
Separate instance IDs for overlapping objects, semantic regions for scene classes, and hybrid projects mixing boxes with fine polygons.
Retail planograms, geospatial tiles, medical imaging, and AV lane graphs staffed with reviewers who understand domain edge cases.
Multi-tier review, boundary distance audits, and golden polygons for classes with high error cost in production.
Million-vertex programs with encrypted pipelines and flexible exports to your MLOps stack.
Tell us your modality, region types, and accuracy requirements — we design polygon annotation pilots with measurable QA gates before production volume.
A proven calibration-to-production workflow for enterprise annotation programs.
01
Upload raw images, video, text, audio, or LiDAR securely — we ingest from cloud storage, SFTP, or your existing ML pipeline.
02
We define labeling guidelines, class taxonomy, edge cases, and accuracy targets with your ML and product stakeholders.
03
Trained annotators label bounding boxes, masks, tracks, transcripts, or 3D cuboids in your toolchain or our workspace.
04
Multi-pass review, consensus scoring, and automated checks before any dataset reaches your training jobs.
05
Receive COCO, JSON, Pascal VOC, or custom exports — plus ongoing support as your models and taxonomies evolve.
Answers about scope, quality, tooling, and delivery.
Segmenting irregular objects, shelf regions, road lanes, building footprints, and anatomical structures where rectangles are insufficient.
Boundary distance checks, overlap rules, blind review on complex shapes, and consensus on ambiguous region edges.
Yes — COCO instance segmentation, custom JSON, and rasterized masks for training pipelines.
Talk to our enterprise team about volume, timeline, QA targets, and pricing.