Human teams for AI companies. We collect the data you can't scrape, label it to your schema, and run the evals that prove your AI works in production. To your spec, in your format, in your bucket.
Get your AI auditedEgocentric/exocentric video, teleoperated demonstrations, multi-modal streams and if it is online, expert dataset curation.

Operator-driven robot demonstrations to your task spec.
Head/chest-mounted capture of real people doing real work
Synced video, audio, and sensor streams - whether it is from the field or the internet.
Every capture run ships through the labeling pipeline below — or straight to your bucket as raw data.
Each one captured while running a real business.
Send us raw data from your pipeline — or route it straight from a collection run above. Annotators label it to your schema and every label passes our multi-step QA.
Detection, segmentation, video, transcripts, domain-specific schemas
Speech transcribed, speaker-diarized, verified
RLHF preference data — ranked, with rationales
Double-checking existing labels and model-generated labels where accuracy is the product
The dataset in your format — raw + annotations.
Full manifest: hours, environments, subjects, capture conditions.
QA report: what was checked, rejected, re-collected.
Schema doc.
Delivered to your S3/GCS bucket. You own the data outright.
A US-based computer-vision company came to us after their previous annotation vendor couldn't hold accuracy at production standards. We built a dedicated team trained on their documentation, with team leads monitoring quality continuously.
“The data is consistently accurate, and the team is extremely professional. We're impressed by how seriously the team approaches accuracy.”
You brief one ops lead. Staffing, training, and scaling are our problem.
Automated checks only work when they agree with human judgment — and that agreement goes stale with every model update. We build and maintain the human side: golden sets, review panels, and verification for models in production. We're the humans.

The definition of “correct” for your task — pressure-tested until independent graders agree.
Where your automated scoring agrees with human judgment, and where it doesn't.
Trained reviewer panels for structured evaluation and preference judgments — blind, independent, agreement-tracked.
Human-graded test sets to check every new model version against.
Side-by-side comparisons of models, versions, or vendors — graded by the same humans, same rubric.
Your tooling or ours — eval platforms, labeling stacks, or a spreadsheet if that's what works.
Need a place to start? We run fixed-fee, 2–4 week independent evaluations — a defensible failure rate, a catalog of how your AI fails, the rubric, and a report your customer can check.
Get your AI auditedA standing team grades your live outputs weekly — scorecard included, new failure types flagged.
Recalibrated after every model update, because yesterday's numbers stop being true when the model changes.
An independent team tries to make your AI fail, in the ways automated testing misses.
Format, completeness, and consistency validation on every item.
Every item checked by a reviewer, not a sample.
An independent team attacks a 25% sample, hunting for what passes one and two.
Reviewers must independently agree before they touch your data — and we report the agreement stats. Ask any other vendor for theirs.
Rejected work is redone before it reaches you. The QA report ships with every delivery.
“We're impressed by how seriously the team approaches accuracy. The data quality has been excellent.”
“Good, reliable people with very little mental overhead. The quality is amazing for the price.”
“They were able to setup a team of 30 for me over the weekend.”