How We Annotate and QA So You Can Trust Every Label
TLDR. Every label you receive is machine drafted, confirmed by a trained annotator, checked again by an independent reviewer, and shipped with overlay previews and a checksum manifest tied to your exact files.
When you train on custom data, you need proof. Not a promise of quality. Proof you can open, scan, and verify.
That is what this post covers. How each label gets made, how it gets checked, and what you receive so you can confirm it yourself.
Machine draft, human decides
We use models to move fast on the first pass. Transcription, bounding boxes, segmentation, event marking. The draft arrives in minutes.
A trained annotator then reviews every item, corrects it, and confirms it. That human decision is the label. You never receive raw model output as final.
If your guidelines change, you update the instructions once. The team applies the new rule across your set the same way, and corrections get tracked against the original draft.
Independent review where errors cost most
Some errors cost more than others. Boundary calls, ambiguous cases, task rules you wrote for a reason.
A second reviewer checks those files independently. Rework goes back to the annotator with an explanation, not a silent fix. Final labels reflect your interpretation.
You can ask for adjustments before delivery is final, and you can see how each decision maps to your spec.
Visual previews you can check in minutes
You should not need to open every annotation file to know if the work is right.
For vision and video, including HD to 4K material, we send overlay previews with labels drawn on frames. You can scan for misaligned boxes, missing instances, or a misunderstood instruction fast.
For speech in English, Hindi, Kannada, Telugu, Tamil, Malayalam, Marathi, and Sanskrit, plus more on request, you get transcripts aligned to audio segments. Flag an issue directly on the segment and we fix it.
Per-file manifest with checksums
Every delivery ships with a manifest. It lists each file, its checksum, its metadata, and its annotation status.
It covers all modalities in your pack. RGB, depth, LiDAR, sensor and radar streams, and multimodal sets where they apply. Compare checksums on receipt. If anything is missing or altered in transit, you know right away.
What you approved is what you received. Trace any label back to its source file and its review path.
Start small and verify it yourself
Start with a paid pilot and put this process to work on your own data. Both sides evaluate fit before scaling.
