A resume is a claim. A contract is a fact.
Most AI-training talent is vetted with a resume, a LinkedIn profile, and a contractor agreement. None of that confirms what someone actually did in their last job, how long they stayed, or whether they were any good at it. We can confirm it — because we were the employer.
- Role · from contract
- CNC Operator — Aerospace
- Tenure · payroll-validated
- 4 yrs 2 mo
- Languages · confirmed
- Portuguese (native) · English (C1)
- Employment
- Local entity · fully compliant
- Engagement history
- 9 projects · renewed 8×
Ground truth · not self-reported
The ground truth we hold
Rainbow has run payroll, compliance, and employment for a network of 15M+ professionals across 120 countries, over more than a decade. For a meaningful share of that network, verification isn't a lookup we buy — it's data we generated:
Roles from contracts
Real roles and responsibilities pulled from actual employment contracts — not self-reported titles polished for a profile page.
Validated tenure
Employment periods confirmed by the payroll that paid them. How long someone actually stayed, everywhere they stayed.
Satisfaction history
Engagement and performance signal accumulated across years of real employment — who was good at the job, not who claims to be.
Direct contact
Verified, current contact with the actual person — no intermediary accounts, no possibility of a stand-in doing the work.
Confirmed language fluency
Fluency established from where people lived, worked, and were paid — the strongest native-speaker signal that exists.
Why identity is the weak link
Anthropic recently disclosed that DeepSeek, Moonshot, and MiniMax used roughly 24,000 fake accounts to distill Claude's outputs into training data for competing models. That was a verification failure on the input side of the pipeline.
The same failure mode exists on the human side of every RLHF pipeline: without ground-truth employment data, a vendor has no better way of confirming who's actually doing the work than anyone had of confirming who was behind those accounts.
When the person grading your model's medical answers might be anyone with a browser, the label quality isn't the biggest risk — the label provenance is.
Employed, not exposed
Most data-labeling and RLHF vendors source contributors as independent contractors. That means misclassification exposure, and no clean answer when a regulator asks who is legally responsible for the person doing the work. Worker-classification claims are now actively being litigated in this industry.
We already run the alternative at scale: payroll, tax, and legal liability infrastructure across 180 countries. Your workforce is employed and paid properly — and the compliance risk sits with us, where it belongs.
Verified identity
The person doing the work is the person we employed — with the record to prove it.
Proper employment
Payroll, tax, and benefits through real entities in-country. No 1099 gray zone.
Liability with us
If anyone asks who's legally responsible for the workforce, the answer is us — in writing.
How an engagement runs
Define
Domains, languages, volumes, and quality bar — scoped with our research team.
Match
We staff from verified employment records: contract-confirmed roles, tenure, languages, and track record.
Employ
Every contributor engaged through compliant employment rails in their country. Zero classification risk on your side.
Deliver & audit
Stable pods, documented QA, and provenance you can trace to a real, named, verified human — for every unit of data.
Who's building this
Rainbow is built and led by its co-founders.
Danny Grander
Co-founder
Shlomy Amsalem
Co-founder