Your Model Is Only as Good as Your Annotators. Verify Them.
A single person operating dozens of accounts poisons your training data at scale. VerifyYou makes that mathematically impossible before a single task is assigned.
The Silent Threat Inside Your Training Data
When one person operates dozens of accounts, your model learns their biases, not the wisdom of a diverse crowd. Most platforms have no way to detect it.
Rampant
Account Renting & Sybil Attacks
Annotators sell verified accounts or create dozens of fakes. A single person operating 50 accounts corrupts your training data at scale.
Invisible
Model Quality Corruption
When one person labels thousands of examples through duplicate accounts, their individual biases become systemic biases in your model.
Costly
Sanctions Violations
OFAC and export control regulations require you to know who is labeling your data. Multi-accounting makes compliance impossible.
Phone verification, email checks, and IP blocking all fail against determined bad actors. SIM farms cost $2 per number. Antidetect browsers are free. The only thing they cannot fake is a unique human being.
One Human. One Account. Every Time.
VerifyYou creates a cryptographic proof that each annotator is a unique human. No government ID. No document upload. Just math.
Annotator verifies once
A 15-second flow proves the annotator is a real, unique human. No government ID required. Works globally.
Unique identity token issued
Each verified human gets a cryptographic token. Same person = same token, even across different platforms.
Duplicates and sanctions blocked
Before any task is assigned, the platform confirms the annotator is unique and not on OFAC sanctions lists.
Annotator verifies once
A 15-second flow proves the annotator is a real, unique human. No government ID required. Works globally.
Unique identity token issued
Each verified human gets a cryptographic token. Same person = same token, even across different platforms.
Duplicates and sanctions blocked
Before any task is assigned, the platform confirms the annotator is unique and not on OFAC sanctions lists.
VerifyYou is the Anti-Sybil Layer for AI Training
Not another fraud score. Not another captcha. VerifyYou is the cryptographic identity layer that makes Sybil attacks mathematically impossible, so your models learn from genuinely diverse human perspectives.
What Changes When You Verify Annotators
From reactive quality filters to proactive identity enforcement.
Protect Model Quality
Guarantee annotation diversity by ensuring every label comes from a unique human perspective, not the same person 50 times over.
Sanctions Compliance
Built-in OFAC screening on every verification. Demonstrate compliance without building your own sanctions infrastructure.
Scale Without Quality Loss
Grow your annotator pool to 100K+ while maintaining the same uniqueness guarantees. No manual review bottlenecks.
Economics That Work
At $0.01 per verification, the cost of proving annotator uniqueness is negligible. The cost of not doing it is a biased model shipped to millions of users.
At Reddit, we fought multi-accounting every single day. The AI labeling industry has the same problem, but the consequences are worse: biased models shipped to millions of users. VerifyYou makes Sybil attacks a solved problem.
Marty Weiner
CTO & Co-Founder · Former CTO, Reddit · Founding Engineer, Pinterest
See What You Could Save
Adjust the inputs to match your annotator pool and see the projected impact.
Your Numbers
Projected Savings
The Problem Is Well-Documented
Researchers and industry observers have been raising alarms about Sybil attacks and annotation quality for years.
“Sybil attacks on crowdsourcing platforms can reduce annotation quality by up to 30%, introducing systematic biases that propagate through model training.”
— Cornell University
“The AI industry has a ghost worker problem. Account sharing and multi-accounting are endemic to annotation platforms, and most platforms lack the tools to detect it.”
— AlgorithmWatch
“Diverse annotator pools are essential for reducing model bias. When duplicate accounts dominate labeling tasks, the resulting models reflect a narrow set of perspectives.”
— Hugging Face Research
VerifyYou vs. The Alternatives
See how cryptographic identity verification compares to legacy approaches for annotator management.
| Capability | VerifyYou | Phone / Email Verification | ID Document Upload | IP / Device Fingerprinting |
|---|---|---|---|---|
| Proves unique identity | ||||
| Defeats multi-accounting | ||||
| OFAC sanctions screening | ||||
| No government ID required | ||||
| Works globally | ||||
| Cost per check | $0.01-0.03 | $0.01-0.05 | $1.00-5.00 | $0.05-0.15 |
Built For Every AI Training Workflow
Whether you run an annotation marketplace, an RLHF pipeline, or in-house labeling, VerifyYou fits your stack.
RLHF Platforms
Reinforcement Learning from Human Feedback requires genuine human diversity. Sybil attacks create echo chambers in your reward model.
Annotation Marketplaces
Annotation marketplaces need to guarantee one person per account to maintain quality for enterprise clients. Uniqueness is a selling point, not just a safeguard.
In-House AI Teams
When you hire contractors for internal labeling projects, verify each one is unique before they touch your proprietary training data.
Frequently Asked Questions
Verify Annotators in Minutes
A single API call during onboarding. Uniqueness guaranteed.
curl -X POST "$CONNECT_API_BASE/v1/business_connect_user/register" \
-H "Content-Type: application/json" \
-H "API-KEY: $CONNECT_API_KEY" \
-d '{
"reference_user_id": "session_abc123",
"redirect_url": "https://yourapp.com/survey/done",
"uniqueness_region_id": "survey_456",
"data_request": {
"type": "GET_HUMANNESS_SCORE_AND_UNIQUE_HUMAN_ID"
}
}'Stop Training AI on Fraudulent Labels
Every Sybil account in your annotator pool is a bias your model will learn and repeat. Stop it before the first label is assigned.
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