How it works

A pipeline built for quality and consent.

Six controlled stages take raw expertise to a model-ready dataset — scoped to your spec, delivered in your format.

01

Source

Vetted experts matched to your spec.

02

Capture

Real work, real sites, clear guidelines.

03

Privacy

Faces and PII blurred or redacted.

04

Label

Domain experts annotate and explain.

05

QA

Multi-pass review, senior arbitration.

06

Deliver

Your schema to S3 or Hugging Face.

01 · Source experts

We scope the skills, demographics, and environments you need, then recruit and vet working practitioners to match.

  • Skill and credential checks
  • Signed contributor rights agreement
  • Paid fairly for their expertise

02 · Capture

Contributors record real tasks or author expert content following clear, written guidelines and pilot feedback.

  • Standardized capture guidelines
  • Device and format specs per program
  • Site/owner consent where filming

03 · Privacy

Before data moves downstream, faces, bystanders, and personally identifiable information are blurred or redacted.

  • Face and bystander blurring
  • PII redaction in audio/text
  • Flagged items excluded from delivery

04 · Expert labeling

Domain experts — not generic crowd workers — annotate steps, timestamps, narration, rubrics, and labels.

  • Your taxonomy and schema
  • Time-aligned, structured labels
  • Expert notes and rationale

05 · QA

Multi-pass review against your guidelines, with disagreements escalated to senior reviewers.

  • Independent second-pass review
  • Spot checks and calibration
  • Feedback loop with your team

06 · Delivery

Clean, documented data in the format your pipeline expects, delivered securely.

  • JSON, JSONL, CSV, parquet, COCO
  • Hugging Face (private), S3, GCS
  • Data card and schema docs
Engagement

Start small. Scale what works.

  • Scope — a short call to define the data, schema, and success criteria.
  • Pilot — a sample set in your format so you can evaluate quality.
  • Scale — ongoing batches with agreed throughput and QA targets.
Every delivered recordYour schema
Source
Domain, task, modality, expert role & experience
Content
Media or text, with timestamps where relevant
Labels
Steps, actions, objects, intents, rubrics, scores
Expert notes
Rationale, technique, edge cases
Privacy
Face blur & PII redaction flags
Consent
Contributor agreement & site/owner consent
QA
Review passes and approval status
Format
JSON · JSONL · CSV · parquet · COCO
Start with a sample

See the data before you commit.

Tell us what your model needs to learn. We’ll scope a pilot and send a sample set in your schema.