One account per person, whatever the email.
Sign-up routing
Workflows
One fixed rule treats every risk the same.You set what happens at each level.
You decide from the dashboard what happens at each risk level: low risk goes straight through, elevated risk takes an anonymous HumanCheck, and the rest waits for your team.
The problem
Check everyone, and good users pay for it. Check no one, and fraud gets through.
- LowElevatedHighSame check, every time
One rule for every risk level
Treating every user the same means either checking everyone, which adds friction for good users, or checking no one, which lets fraud through.
- Needs a developerBlock duplicatesiLast changed11 months ago
Hand-built rules get stale
Hard-coded logic can't flex as new risk patterns show up, so teams end up patching rules by hand.
- Good?BadThe middle has nowhere to go
No place for the risky middle
Clear-good and clear-bad cases are easy; without a defined workflow, the risky middle has nowhere to go.
How it works
Low risk, elevated risk, review: you draw the lines
01
Passive signals score every visit
Signals run first, quietly, and build each person's risk score before any decision gets made.
02
Elevated risk routes to an anonymous HumanCheck
Your workflow decides who sees a check: low risk gets let in, elevated risk takes a selfie of about 3 seconds.
03
Change it anytime, no redeploy
Let low risk in, hold the rest for review, and change the rule anytime from the dashboard, no redeploy needed.
This visit
•SignalsDevice, network, behaviorScoring
No decision yet
The platform
Passive signals and anonymous HumanCheck, routed by you.
A solution for every scenario
Let's talk trust
Start with a free pilot: see what the check catches on your own users, with no dev work to begin.
With tools like VerifyYou, AI becomes a force for good. It helps prevent AI-driven fraud and can be done with much lower friction than what we built over the years.
Built by humans from
- Quora
- Square
- Gusto
- Cohere Health
- Accenture
Security
Privacy and security in every decision
We keep a face code, not the photo, and hold it under SOC 2 Type II and ISO 27001:2022 compliant controls.
No images stored. VerifyYou does not store images.
No ID or documents. HumanCheck anonymously confirms there is a real, live, unique person, with no name or ID.
A face code, not a photo. We treat it as sensitive biometric data.
SOC 2 Type II
ISO 27001:2022
ISO/IEC 30107-3 PAD Level 1 & 2
GDPR compliant
Common questions
Who sets the rules?
You do, from the dashboard, with no code: your settings decide where the pass line sits and what happens at each risk level, including verification limits, the outcome on a duplicate and a minimum age gate.
What actually triggers the anonymous HumanCheck?
Your settings do. Passive signals score every visit: below your pass line people go straight through, above it they take an anonymous HumanCheck of about 3 seconds, and anything you choose to hold waits for your team.
What happens to someone held for review?
They land in your team's queue rather than being blocked automatically. From there your team can tag the account, batch it with similar cases, or schedule it for a re-check later.
Can we change a rule without a redeploy?
Yes. Verification limits, outcomes, the age gate and uniqueness scope are all dashboard settings, so a change takes effect on your next check, not your next release.
Do you store photos of our users?
No. We keep a face code, not your users' images.
How much work is it to add?
It's two API calls: one to start the check and one to read the result.
Do we have to replace our KYC or bot tools?
No. It sits on top of your stack, so you keep your KYC and bot tools.
What does it cost?
It's 50 to 100 times cheaper than KYC. You pay per check, and nobody uploads an ID document.
Resources
Keep learning
Blog
The Best Human Verification Platforms in 2026
Human verification covers four different product categories that solve four different problems. This guide separates them, compares what each one confirms, and shows which published prices vendors actually disclose.
Read the post
Blog
Sybil Attacks in AI Data Labeling: How Platforms Prevent Them in 2026
How one person running many annotator accounts distorts training data, how the major labeling platforms defend against it today, and how the tools built to stop it compare.
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Blog
The Most Expensive Part of an Odyssey Ticket Is the Missing Verification
The Odyssey sold out every IMAX 70mm screening in New York, and the resale chaos that followed shows what happens when scarcity meets anonymity.
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Ready to get started?
VerifyYou scales trust: passive signals on every session, a face check only when risk calls for it.
