A resume is a claim. A system of record is a fact.
Most AI-training talent is vetted with a resume, a LinkedIn profile, and a contractor agreement — none of which confirms who someone actually is, who they work for, or how senior they are. Rainbow verifies each expert's identity, employer, role, and seniority at source, against systems of record.
- Identity
- Verified · at source
- Employer
- Verified · at source
- Role
- CNC Operator — verified
- Seniority
- Senior — verified
- Languages
- Portuguese (native) · verified
Ground truth · not self-reported
What we verify
Four properties, established before an expert touches your data — and attached as provenance to every unit of work:
Identity
The person doing the work is who they claim to be — confirmed against authoritative records, not a profile photo.
Employer
Where they actually work — verified at source, not read off a resume.
Role
What they actually do — the real function, not the title typed into a form.
Seniority
How senior they actually are — established against systems of record, not self-attestation.
Provenance is the weak link. We made it the product.
In every human-data pipeline, the hardest thing to confirm is who actually did the work. Resumes, marketplace profiles, and contractor sign-ups don't establish identity, employer, role, or seniority.
A qualification test verifies a session, not a person. A profile verifies nothing at all. Verification at source closes that gap — and the provenance ships with the data, unit by unit.
We publish what is verified and to what standard. The mechanics stay ours.
The standard
Verification happens at source, against systems of record — not by collecting documents from the expert and taking them at face value.
Every engagement ships with provenance: which verified expert did the work, under which verified role and seniority, with the QA trail behind it.
How an engagement runs
Define
Domains, languages, volumes, and quality bar — scoped with our research team.
Match
We staff from verified experts: role, seniority, language, and track record.
Verify
Every expert is verified at source before their first task — identity, employer, role, seniority.
Deliver & audit
Stable teams, documented QA, and provenance you can trace to a named, verified professional — for every unit of data.
Questions, answered
- What does “verified at source” mean?
- Each fact about an expert — identity, employer, role, and seniority — is confirmed against a system of record: a license registry, the employer itself, an authoritative database. Not against documents the expert supplies. A resume is a claim; a system of record is a fact.
- What exactly does Rainbow verify about each expert?
- Four properties, before their first task: identity (the person is who they claim to be), employer (where they actually work), role (what they actually do), and seniority (how senior they actually are). The verification is attached as provenance to every unit of work they produce.
- What is data provenance in AI training data?
- Provenance is the link between a unit of training data and the verified person who produced it — including the role and seniority they were verified under and the QA trail behind the work. With per-unit provenance, any row in a dataset can be walked back to a named, verified professional.
- Why isn’t a resume or a qualification test enough?
- Resumes and profiles are self-reported claims, and a qualification test verifies a session, not a person — it cannot detect account sharing or subcontracting after onboarding. Verification at source inverts the direction of trust: the confirming party has no stake in the contributor being hired.
Who's building this
Rainbow is built and led by its co-founders.
Danny Grander
Co-founder
Shlomy Amsalem
Co-founder