Ground truth. No matter what it takes.

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 audited

Built by people who've done this before.

1,000+ DEPLOYED IN HUMAN-IN-THE-LOOP OPS
Pear VCY CombinatorInsight PartnersStartXForbes 30 Under 30NeoStanford
01
COLLECTION

Data you can't scrape.

EGOCENTRIC · TELEOP · MULTIMODAL · FIELD AUDIO
I need data that doesn't exist →
02
LABELING

Raw data in. Training data out.

DETECTION · SEGMENTATION · TRANSCRIPTS · RLHF
I need data labeled →
03
Evaluation

Graded by humans.

GOLDEN SETS · BENCHMARKING · RED TEAMING
I need proof my model works in production →
01 / COLLECTION

The data you can't scrape.

Egocentric/exocentric video, teleoperated demonstrations, multi-modal streams and if it is online, expert dataset curation.

WHAT WE COLLECT
Viewfinder diagram
01.

HITL & teleoperation

Operator-driven robot demonstrations to your task spec.

02.

Egocentric video

Head/chest-mounted capture of real people doing real work

03.

Multimodal capture

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.

Datasets / Index

Datasets we already built.

Each one captured while running a real business.

Library / Datasets
01–04 / 04
01

Plastic surgery consultations

Prospective patients talking to the reps and surgeons who handled them. For teams building medical intake or consultative sales models.

Volume
380,000 chats
Counterparty
Reps and surgeons
Request a sample →
02

Sales RL environment

Our own sales activity, anonymized and structured as a reinforcement learning environment. Every rep action is recorded.

Span
6 years
Recorded
Per-rep actions
Request a sample →
03

Short-term rental guest chat

Host and guest conversations on Airbnb and VRBO, including the threads that continued off platform.

Bookings
1,000+
Listings
6 · Airbnb + VRBO
Request a sample →
04

Slack Team Conversations

Internal Slack from a 300-person offshore operations team — how work actually gets escalated, handed off and closed. For teams building agents that work inside a company.

Headcount
300+ people
Messages
1.1M+ monthly
Request a sample →
02 / LABELING

Raw data in. Training data out.

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.

WHAT WE LABEL
01.

Custom annotation

Detection, segmentation, video, transcripts, domain-specific schemas

02.

Transcription & diarization

Speech transcribed, speaker-diarized, verified

03.

Preference/taste data

RLHF preference data — ranked, with rationales

04.

Label verification

Double-checking existing labels and model-generated labels where accuracy is the product

WHAT YOU RECEIVE
01.

The dataset in your format — raw + annotations.

02.

Full manifest: hours, environments, subjects, capture conditions.

03.

QA report: what was checked, rejected, re-collected.

04.

Schema doc.

05.

Delivered to your S3/GCS bucket. You own the data outright.

FORMATS — WEBDATASET · COCO · ROS BAGS · PARQUET · JSONL · OR EXACTLY WHAT YOUR PIPELINE EXPECTS
DATASET MANIFEST
DATASET
[CLIENT]_EGOCENTRIC_V1
MODALITY
EGOCENTRIC VIDEO
VOLUME
2,400 HRS · 85 ENVIRONMENTS
LABEL SCHEMA
[CLIENT-DEFINED] V1.2
QA
3-PASS · ADVERSARIAL
FORMAT
YOURS
DELIVERY
S3://YOUR-BUCKET/
QA RECORD
PASS 1 — AUTO CHECKS
100% FRAMES
PASS 2 — HUMAN REVIEW
100% LABELS
PASS 3 — ADVERSARIAL AUDIT
25% SAMPLE
FLAGS RAISED
412
FLAGS RESOLVED
ALL
STATUS
SHIPPED TO SPEC
EXAMPLE DELIVERY MANIFEST — WHAT LANDS IN YOUR BUCKET.
CASE STUDY — PILOT ENGAGEMENT

From one project to four production programs.

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.”

— CO-FOUNDER, US-BASED AI COMPANY
100+
ANNOTATION SPECIALISTS DEPLOYED
1 → 4
PRODUCTION PROJECTS
100,000
FRAMES PER PROJECT
100%
ACCEPTANCE RATE — PROJECT 1
111,550
FRAMES QA-PASSED IN JUNE — VS 100,000 REQUIRED
ROAD & DRIVABLE-AREA ANNOTATION · OBJECT DETECTION · CONCRETE SEGMENTATION · AERIAL IMAGERY · BLUEPRINT INTERPRETATION
PRICED ON COMPLETED WORK — NOT HEADCOUNT
1-DAY LEAD TIME FROM KICKOFF TO FIRST PRODUCTION-QUALITY BATCH

You brief one ops lead. Staffing, training, and scaling are our problem.

03 / EVALS

The human side of evaluation.

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.

WHAT WE BUILD
Inspection grid diagram
01.

Rubrics & golden sets

The definition of “correct” for your task — pressure-tested until independent graders agree.

02.

Automated vs. human

Where your automated scoring agrees with human judgment, and where it doesn't.

03.

Human eval panels

Trained reviewer panels for structured evaluation and preference judgments — blind, independent, agreement-tracked.

04.

Test sets for releases

Human-graded test sets to check every new model version against.

05.

Benchmarking

Side-by-side comparisons of models, versions, or vendors — graded by the same humans, same rubric.

06.

Tooling

Your tooling or ours — eval platforms, labeling stacks, or a spreadsheet if that's what works.

03 / EVALS
INDEPENDENT EVALUATION

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 audited
PRODUCTION EVALS
01.

Production sampling

A standing team grades your live outputs weekly — scorecard included, new failure types flagged.

02.

Drift watch

Recalibrated after every model update, because yesterday's numbers stop being true when the model changes.

03.

Red teaming

An independent team tries to make your AI fail, in the ways automated testing misses.

QA / THE foundation

One discipline behind everything.

PASS 1
AUTOMATED CHECKS

Format, completeness, and consistency validation on every item.

PASS 2
HUMAN REVIEW

Every item checked by a reviewer, not a sample.

PASS 3
ADVERSARIAL AUDIT

An independent team attacks a 25% sample, hunting for what passes one and two.

ONGOING
CALIBRATION, ON THE RECORD

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.

04 / CONTACT
WHAT CUSTOMERS SAY

“We're impressed by how seriously the team approaches accuracy. The data quality has been excellent.”

CO-FOUNDER, US-BASED AI COMPANY

“Good, reliable people with very little mental overhead. The quality is amazing for the price.”

CEO, SOFTWARE COMPANY

“They were able to setup a team of 30 for me over the weekend.”

CTO, DATA FOUNDRY

Great models need clean data.