# How the check works
Source: https://mr.verifyyou.com/how-it-works
Liveness and uniqueness in a few seconds, no government ID, and the honest line on what it does not do.
A human check is the step you put in front of an action that confirms a real, live, unique human in seconds, and returns a simple verified or denied. It proves humanness and uniqueness. It does not prove a name or a legal identity. Here is the whole of it.
## Two questions, a few seconds
Real and unique are really two questions, and the check answers both in the same moment in the browser.
Is a live person present right now, and not a photo, a recording, or a deepfake. This is what stops a bot or an AI-generated respondent from clearing the door.
Have you already seen and paid this person before. The new respondent is compared against the ones you have already verified, which is what catches duplicates and survey farms.
A duplicate fails on uniqueness, a bot fails on liveness, and a survey farm fails both the moment the same face appears again.
## You decide what counts as unique
Uniqueness is only useful if it matches the way you run studies, so it is yours to scope. Ask for uniqueness per survey, per study, or per panel, whatever bucket fits the question you are answering. Need one response per person within a single survey? Scope it there. Protecting a standing panel from the same human enrolling twice? Scope it to the panel.
## Genuine repeat respondents are not punished
A real worry with any verification step is that you will add friction for your good participants. You do not. A respondent who passes carries a portable credential across your surveys, so a genuine returning participant is recognized rather than asked to prove themselves from scratch. The work happens on the first visit. After that it is continuity, not a fresh hurdle.
## Put the check right before the incentive
Where you place the check matters as much as the check itself. For conversion, the placement to reach for is right before the incentive or the payout. By the time a respondent gets there they have done the survey work and want to finish, so real people push through, while the accounts that balk at putting a live, unique face on the line at that moment are usually the ones you were about to pay for nothing. Placement turns the check into a filter on who collects, not a hurdle early in the flow.
## The honest boundary
A human check is additive to your quality stack, not a replacement for it. It sits alongside your attention checks, speeding detection, and geo and device signals, and answers the question most of those cannot: whether there is a real, unique, live human behind the response at all. It is not KYC and does not prove anyone's name or legal identity, on purpose. For survey work you almost never need to know who someone legally is, only that they are a real person you have not already paid.
## What we keep
To recognize a returning respondent, we keep a face vector, a one-way numerical representation of facial geometry, an array of numbers rather than an image, checked against the ones already held. That number is not a picture and, on its own, is just a string with no name attached. What your dashboard sees is a verdict, verified or denied, never a face. The full detail on what is collected and how long it is kept lives in our privacy policy, which is the document to rely on for retention specifics.
## The easiest way to see it
The best way to know whether the numbers hold for your audience is a free pilot with a branded share link, a check you push to a batch of your own respondents with no engineering work, so you can watch real people run through it before writing integration code. If that is useful, [book a chat with us](/meet-us).
# Talk to us
Source: https://mr.verifyyou.com/meet-us
Book a chat with us or grab the assets.
The fastest way to see whether VerifyYou fits your panels is a quick chat. Fifteen minutes is plenty for a first conversation, whether we are meeting at an event or on a call.
## What fifteen minutes looks like
No slides unless you want them. We would rather hear how you run studies, where drop-off and duplicates actually cost you, and whether a real, unique, live human at the door would help. If it looks like a fit, the next step is usually a free pilot with a branded link you push to a batch of your own respondents, so you are looking at your own numbers rather than ours.
## Take the assets with you
The Human Data Premium, the longer argument for proving a real human at the door.
The whole thing on a single page, easy to forward or print.
How liveness and uniqueness work in a few seconds, and how to get a branded pilot link for your respondents.
## Where this goes
Book a time at [sales.verifyyou.com](https://sales.verifyyou.com/), or email David Graham at [david@verifyyou.com](mailto:david@verifyyou.com). Whatever you send lands with a person, not a queue, and you will hear back. If you are reading this after the show, a call works just as well as the hallway would have.
Best,
The VerifyYou team
# VerifyYou for market research
Source: https://mr.verifyyou.com/one-pager
The whole thing on one page. Real, unique, live humans in your panel, before the incentive is paid.
**Stop paying for the same person twice, and for bots not at all.** VerifyYou confirms that each respondent is a real, unique, live human before they reach your study or your incentive, without collecting extra PII. The check takes a few seconds in the browser, needs no government ID, and what your dashboard sees is a verdict, never a face.
## The problem, in third-party numbers
\~40%
of nonprobability survey interviews in 2025 were fraudulent, on the order of two billion responses.
Insights Association
99.8%
of standard survey quality checks passed by AI agents in one proof-of-concept study.
Westwood, PNAS
51%
of all internet traffic is now automated, surpassing human traffic for the first time.
Imperva, 2025
These are industry figures from third parties, not our measurement of your account. Generative AI lowered the cost to fake a person, so the volume keeps rising, and passive detection after the fact is a race you never fully win.
## What it is
Two questions answered in the same few seconds. **Liveness:** a live person is present now, not a photo, a recording, or a deepfake. **Uniqueness:** you have not already seen and paid this person, scoped to per survey, per study, or per panel, whichever fits. A duplicate fails uniqueness, a bot fails liveness, a survey farm fails both.
## What it catches
* **Duplicate respondents** taking the same study under different emails or phone numbers.
* **Professional survey-takers** who speed-run incentivized surveys across many panels.
* **Bots and AI-assisted respondents** that clear a simple bot check and read like a thoughtful human.
* **Survey farms**, one human running thousands of accounts across platforms.
## What it is not
Not KYC. It does not prove a name or a legal identity, on purpose, because for survey work you almost never need to know who someone legally is, only that they are a real person you have not already paid. It sits alongside your attention checks and speeding detection, not in place of them. A face vector is kept to recognize a returning respondent, an array of numbers rather than an image, and genuine repeat participants carry a portable credential so they are not asked to prove themselves twice.
## Where it fits
Put the check right before the incentive. Real people push through because they want to finish, and the accounts that balk are usually the ones you were about to pay for nothing.
## See it for yourself
A free pilot with a branded link, a check you push to a batch of your own respondents with no engineering work, so you can watch real people run through it before writing integration code.
**Let's talk.** Book a time at sales.verifyyou.com, or reach David Graham at [david@verifyyou.com](mailto:david@verifyyou.com). Longer version in [the white paper](/white-paper).
# The respondent experience
Source: https://mr.verifyyou.com/respondent-experience
One screen at a time: the click, the scan, the pass, and the gate opening into your study. Try the real check yourself.
Try it yourself
See the real check, right now
This is exactly what your respondents go through. No signup, no integration, just the experience, and it takes a few seconds.
Four steps, one screen at a time. Every screen below is the real product unless it is marked otherwise.
From your panel invite, or a link in your survey platform, Alchemer here, wherever you recruit. The link opens the check first, not your study.
A few seconds in the camera with live prompts. No photo is shared with you or with your survey platform, only a yes or no.
The scan ends one of two ways, and both are instant.
The scan ends
PassFail
Straight into your study
Screened out. Nothing to pay.
They pass
Live and unique. They go straight on to your survey with a one-time password already filled in, then your questions. (Real product, demo partner brand.)
The pass lane continues here: your survey's login page, password already in the box. With auto-submit on, they skip this page entirely. (Illustration; the real page inherits your study's theme.)
They fail
A duplicate face, a replay, or a bot. No password, no study, no complete, nothing to pay. They are routed to your panel's screen-out, and a new device or browser changes nothing, because the check recognizes the face. (Real product.)
The fail lane ends here, by design. There is no page after this one.
Same study: a dead end
Their face already earned its one password, and it is spent. One person, one response, permanently.
Your next study: welcome back
Recognized, less friction, and a fresh one-time password for the new study. You are never charged twice for the same human.
## Barely any friction for the people you want
The whole point is that a genuine respondent hardly notices it. Real people clear the scan in seconds and are recognized on the way back, while the accounts you were about to pay for nothing quietly fall away at the door. It is a filter on who collects, not a hurdle for the people you want in your data.
# The problem, in third-party numbers
Source: https://mr.verifyyou.com/the-problem
How much of an online panel is really bots, duplicates, and survey farms, using published research rather than our own figures.
It is a fair question to ask before you change anything: how much of the online data you collect is actually from real, unique people, and how much is bots, duplicates, and synthetic respondents? This is a plain look at that, using independent published research rather than our own numbers. Everything below is an industry figure from a third party, not something VerifyYou measured in your account, and we will be clear each time about whose number it is.
## What the independent research says
The single source we point people to most is a research brief from NORC at the University of Chicago, a long-standing social-science research organization. NORC puts survey-fraud rates at roughly 15 to 30 percent across the industry, and up to about 45 percent on some platforms. On a typical online panel, that means somewhere between one in seven and one in three responses may not be from a genuine, unique respondent, and on the worst platforms it can approach half.
That is not an outlier reading. The Insights Association estimated that around 40 percent of all nonprobability survey interviews in 2025 were fraudulent, on the order of two billion responses. Kantar, one of the largest research firms in the world, went as far as to call panel fraud "the new ad fraud," and has found researchers discarding up to 38 percent of collected data on average over quality problems. Greenbook has written about the pervasive threat of tech-enabled fraud in survey research. Read together, these sources describe roughly the same range, from about a third to as much as half of responses on some platforms.
These are industry figures from NORC, the Insights Association, Kantar, a Dartmouth study in PNAS, and Greenbook, not VerifyYou measurements and not a claim about your specific account. They describe how much junk is typically sitting in online data, so you can judge whether the problem is worth solving for you.
## Why it is getting worse, not better
A few years ago, faking a survey response at scale took real effort. Generative AI changed the economics. A large language model can now produce fluent, human-sounding answers in bulk, and a bot farm can spin up convincing synthetic respondents cheaply. The cost to fake a person has fallen, and when the cost of an attack falls, the volume rises. A peer-reviewed Dartmouth study in PNAS found that AI agents can now pass standard quality checks at near-perfect rates, 99.8 percent in one proof-of-concept study, which is why the trap questions many teams rely on are quietly losing their grip.
This is why catching fakes after the fact is such a hard game. Passive detection, the kind that watches behavior and tries to spot a bot from the outside, is a cat-and-mouse loop. Defenders learn the latest tell, attackers learn the new defense, and the synthetic responses look a little more real each round. You can win rounds, but you do not get to stop playing.
## What it costs you
The figures stay abstract until you turn them into line items. When a meaningful share of your data is not from real, unique people, the cost shows up in three places:
* **Wasted incentives and spend.** Every payout paid to a bot or a duplicate is money spent on traffic that was never going to become a real respondent. The fake portion is pure waste.
* **Contaminated datasets.** Fraudulent and duplicated responses do not just miss value, they poison what is left. A decision drawn from a panel that is a third synthetic is drawn partly from noise.
* **Decisions made on data that is not from real people.** This is the quiet one. If you set strategy or product direction off survey results, and a large slice of that data came from bots and survey farms, you are steering by a compass that has been tampered with.
## The shift, from catching fakes to proving a real human at the door
The pattern in all of this is reactive. You collect the data, then go hunting for the fraud inside it, in a race you can never fully win. There is a different posture available, which is to move the question to the door. Instead of asking how much of what you just collected was fake, you ask whether there is a real, live, unique human here, right now, before this counts.
That is the shift from reactive detection to proactive proof, and it is the problem VerifyYou is built for. Next: [how the check works](/how-it-works), or the longer argument in [the white paper](/white-paper).
# What HumanCheck is
Source: https://mr.verifyyou.com/what-humancheck-is
HumanCheck is VerifyYou's camera-based check that confirms a real, unique, live person is behind a response, without collecting a name or an ID. Here is how it works, for market research.
HumanCheck is VerifyYou's camera-based way to tell a real, unique respondent from a bot, a duplicate, or an AI. It confirms two things: that a real, live person is present right now, and that this person is unique, not someone taking your study more than once under different accounts. It does all of that without collecting a name, an address, or a government ID.
## The one question it answers
HumanCheck answers a single question: is there a real, unique, live human on the other end of this response, right now? It does not try to learn who someone is. It focuses only on confirming that a genuine, individual person is present. A respondent points their camera at their face for a brief scan, a few seconds in the browser, with no documents and no government ID.
## The two things it proves
A real person is present in the moment, which is what makes bots and fake media hard to get through. The check looks for the small signals that only a living person produces right then, and is built to reject photos, recordings, a screen showing a screen, and AI generated faces.
One human, one response. This keeps a single person from quietly taking the same study many times under different accounts. When a genuine respondent returns, the check can recognize them, while someone you have removed for cause finds it hard to come back under a new identity.
## The return journey
Verification is not only a one-time gate. It works across visits, so you can be strict where it matters and light where it does not.
The initial check takes seconds and produces a verdict from the face scan: verified or denied.
On later visits you choose how strict to be. Require a fresh scan every time, or quietly recognize a returning respondent by device, so a genuine repeat participant is not made to prove themselves again.
A respondent who confirmed an email or phone during the check can be recognized across devices. Guests, who did not confirm either, are recognized more lightly, and their records fade over time.
## What it is not
It does not verify a legal identity or a name, on purpose. For survey work you almost never need to know who someone legally is, only that they are a real person you have not already counted.
Two-factor proves that someone holds a credential, like a phone or an email. HumanCheck ties the check to the actual person in front of the camera, not to a thing they possess.
It asks for a live presence, which is a stronger signal than passive behavioral analysis.
## Your respondents' data
The scan becomes a face vector, a one-way numerical representation of the face's geometry, used only to recognize a returning person. It is not a stored image. Your panel and your platform never touch any of that. What you receive is a verdict, verified or denied, and the reference id you chose to attach, never a face and never a name.
## Where it fits in a study
Put the check right before the incentive. Real people push through because they want to finish, and the accounts that balk are usually the ones you were about to pay for nothing. It sits alongside the checks you already run, your attention checks and your device signals, and answers the real-versus-fake question those were not built to settle.
Liveness and uniqueness in a few seconds, and the honest boundary.
The real screens a respondent sees, from click to complete, and the demo.
# The Human Data Premium
Source: https://mr.verifyyou.com/white-paper
Why cost per quality response will define the next era of market research, and why provably human data is becoming the scarcest asset in the insights economy.
White paper ยท May 2026
The Human Data Premium
The full designed paper, with the charts and the citations. Thirteen pages, about a fourteen minute read. Good to save, forward to your team, or print for the folder.
Prefer to read here? The web version is below, and it tells the same story.
The value of research has always rested on a quiet assumption: that the people answering are real, that each of them is one person, and that they are who your study needs them to be. That assumption is getting expensive to hold. This is the short, readable version of our white paper, which is about the premium that provably human data now commands, why the market has not priced it in yet, and the one change that recovers most of it.
## The buying metric is changing
For years the industry has bought on cost per complete. That is becoming a weak number, because a complete tells you a response arrived, not that a real person left it. The figure that matters now is cost per quality response, what you actually pay once the bad data is removed, and on that measure the picture is stark.
\~40%
of nonprobability survey interviews in 2025 were fraudulent, on the order of two billion responses.
Insights Association
99.8%
of standard survey quality checks passed by AI agents in one proof-of-concept study.
Westwood, PNAS
51%
of all internet traffic is now automated, surpassing human traffic for the first time.
Imperva Bad Bot Report, 2025
These are industry figures from third parties, the Insights Association, a peer-reviewed PNAS study out of Dartmouth, Imperva, Kantar, and Research Defender among them, not VerifyYou measurements and not a claim about any specific account. The full citations are in the PDF above.
## A large industry, taking on water at the source
Market research is a \$142 billion global industry, and its core promise is simple: gather authentic human perspectives so organizations can make better decisions. The threat to that promise used to be methodological. It has been overtaken by something more literal. A growing share of online respondents are not who they claim to be, and an increasing number of them are not human at all. The U.S. Department of Justice has indicted operators who ran a survey fraud ring for a decade, billing clients ten million dollars in fabricated data, and Research Defender has flagged roughly a third of raw responses across hundreds of sample sources as fraudulent.
## As capital flows into AI, human data becomes scarce
There is a funnel underneath all of this, and it runs one way. Investment in AI drives model capability, capability drives scale and reach, and scale fills the internet and the panels with non-human content. That is what makes verified human data the scarcest, most valuable input in the insights economy, and the market has started to price it. The AI training data market, worth about $2.68 billion in 2024, is projected to reach north of $11 billion by 2030, and human-written content already costs several times its AI-generated equivalent. When the honest share of the world's data shrinks, the premium on the honest share rises to match.
## Three layers of cost, each more expensive than the last
The damage from survey fraud reaches well past the wasted incentive. It operates on three levels, and each one reprices what a study is worth. The first is operational: when a third of responses are fraudulent and traditional cleaning catches only a fraction, teams over-recruit, re-field, and burn labor chasing bad records. The second is decisional: contaminated data that survives cleaning shapes strategy, and a decision drawn from a poisoned set is drawn partly from noise. The third is compounding: every study that ships on bad data erodes confidence in the method itself. Kantar's own tracking shows the share of collected data discarded for quality problems climbing toward the mid-forties in percent, and once you price fraud back in, cost per quality response can run many times the headline cost per complete.
## Detection has a ceiling, verification does not
The industry answered the crisis with an expanding toolkit: device fingerprinting, behavioral analysis, trap questions, CAPTCHAs, and post-collection statistical cleaning. Each catches some fraud. None catches enough. Research Defender's data suggests the large majority of survey fraud now evades traditional cleaning outright, and detection runs on a losing asymmetry: defenders have to catch every form of fraud, while an attacker only has to find one gap. Every technique you add is a target the other side learns to beat.
The deeper point is about the question being asked. Detection answers one thing, is this session automated. Verification answers a different one, is this person real, unique, and present. The first question has a ceiling. The second does not.
## A different model, with different economics
Detection accepts a contaminated input and tries to filter it afterward, which is reactive, expensive, and incomplete by design. Verification moves the question to the door and confirms a real, unique, live human before the first question loads. The two models have genuinely different economics.
| How each model operates | Detection (the old model) | Verification (the new model) |
| --------------------------- | -------------------------------- | --------------------------------- |
| When it is addressed | After data collection | Before the first question loads |
| Bad data in the dataset | All of it, cleaned afterward | Blocked at the door |
| Cost of missed fraud | Contaminated insights | Does not apply |
| Cost trajectory | Scales with fraud volume | Fixed per verified human |
| Duplicate and multi-account | Difficult to detect | One credential, one person |
| AI agents | An arms race with each new model | Cannot produce a human credential |
| Respondent friction | New onboarding on every platform | Seamless across the network |
The cost of detection scales with the size of the problem, and the problem is growing. The cost of verification is fixed per respondent. It also solves something detection ignores entirely, which is respondent friction: one verified credential means one fewer login, one fewer onboarding, one fewer reason to abandon a study.
"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 did over the years."
## What it does not claim
A human check is additive to your quality stack, not a replacement for it. It is not KYC and does not prove anyone's name or legal identity, on purpose, because for survey work you almost never need to know who someone legally is, only that they are a real person you have not already paid. The credential is phone-based, verified through the device in the respondent's pocket, with liveness confirming a real human is present at the moment of the check rather than a replay or a synthetic feed. A respondent who passes carries that credential across your surveys, so a genuine returning participant is recognized rather than asked to prove themselves over again. What reaches your dashboard is a verdict, never a face.
## The bottom line
The market has not fully priced the human data premium yet, which is exactly why it is worth capturing now. The industry is already moving: the 2025 ICC and ESOMAR code now defines a person as a human being, distinct from a synthetic or digitally created persona, and regulation is following. The teams that move the question to the door stop paying for the same person twice, and for bots not at all, and they get to make decisions on data they can actually stand behind. The cheapest way to find out what that is worth for your audience is a free pilot with a branded link, a check you push to a batch of your own respondents with no engineering work, so you can watch real people run through it before writing any integration code.
# Why VerifyYou, for market research
Source: https://mr.verifyyou.com/why-verifyyou
The whole case in one read: what VerifyYou is, why survey fraud has crossed from nuisance to structural failure, where a human check fits, and what your panels get back.
If you only read one page, read this one. It walks through what VerifyYou is, why it matters for panels and studies, and where it fits next to the tools you already run. It is written for research and panel teams, so we will use your language and be honest about the edges.
## The problem, in market research
Trust breaks at scale, and in research the thing being gamed is the incentive. Every completed response can be worth money, so bots, duplicate respondents, professional survey-takers, and survey farms all find their way in, and they have only gotten harder to spot. The old fixes assume you can catch the bad ones after the fact. That assumption is failing, because AI has changed what a fake respondent costs to make. A language model now writes fluent, human-sounding open-ends in bulk, and a farm can spin up convincing synthetic respondents for almost nothing. When the cost to fake a person falls, the volume rises, and the honest share of your data shrinks.
For the scale of it in third-party numbers, see [the problem](/the-problem). The short version is that a large share of online survey data is no longer coming from real, unique people, and cleaning it up after collection is a race you do not get to stop running.
## What VerifyYou is
VerifyYou is a quick face check a respondent does with their phone or webcam. No documents, no government ID, no wallet, just their face, for a few seconds, in the browser. From that one scan it proves two things at once.
A real person is present right now, not a photo, a recording, a screen showing a screen, or an AI generated face. This is what stops a bot or a synthetic respondent at the door.
One human, one response. So one person cannot quietly become fifteen to skew a study, and a screened-out respondent cannot come straight back under a new name.
A duplicate fails on uniqueness. A bot fails on liveness. A survey farm fails both the moment the same face appears again. What your dashboard sees afterward is a verdict, verified or denied, never a face.
## What unique and human gets you
VerifyYou ensures that every respondent is unique and human. That one guarantee quietly closes most of the ways a study gets gamed, at the door rather than after the data is already collected.
No duplicate respondents claiming the incentive twice, no survey farm running fifteen accounts, and no one returning after a screen-out under a new name. Uniqueness is scoped to per survey, per study, or per panel, whichever fits.
No automated scripts and no AI-generated respondents clearing your screeners, because the check confirms a real, present person, not a photo, a recording, or a synthetic face.
Every payout lands with a distinct, genuine respondent, so the fraudulent share that used to be pure waste never enters the funnel in the first place.
Decisions built on responses from real, distinct people instead of noise, so strategy is not quietly steered by bots and duplicates.
## The trust layer between no checks and full KYC
Most teams reach for one of two other tools and find neither fits. Passive bot signals guess at risk but never prove a person, so a determined human still runs fifteen accounts across your panel. Full KYC proves a legal identity, but it brings the cost, the PII, and the friction you were trying to avoid, and for survey work you almost never need to know who someone legally is. VerifyYou sits in the gap. It proves a real, live, unique human without proving who they are, which is exactly what a panel is actually after: not your respondents' identities, just the guarantee that one real person means one response.
## Why detection alone stopped working
The industry answered the fraud crisis with an expanding toolkit: attention checks, speeding detection, open-end review, device and geo signals, CAPTCHAs, and post-field statistical cleaning. Each catches some fraud. None catches enough. A peer-reviewed Dartmouth study in PNAS found AI agents passing standard survey quality checks at 99.8 percent, and detection runs on a losing asymmetry, because defenders have to catch every kind of fraud while an attacker only has to find one gap. VerifyYou does not play that game. Instead of asking, after collection, how much of this was fake, it asks at the door whether a real, unique, live human is here at all. That question does not have the same ceiling.
## The economics of fraud
Fraud is an economic decision. Someone games a survey when faking a presence costs less than the incentive is worth. AI has pushed that cost toward zero, which is why the volume keeps climbing. VerifyYou changes the math at the point of entry. When every respondent has to be a unique, verified human before they reach the incentive, the cost of creating a fake presence climbs sharply and the economics tilt back toward you. You stop paying to clean bad data after the fact and make it uneconomic to create in the first place.
## Built by people who fought this at scale
VerifyYou comes from people who fought this exact problem at internet scale. Marty Weiner, our CTO, was a founding engineer at Pinterest and Reddit's first CTO, where he spent years building systems to fight spam, bots, and abuse across hundreds of millions of users. He watched communities get overrun by fake accounts and ban evaders who spun up new profiles in minutes. VerifyYou is the layer he wished he had: a way for any platform, a survey panel included, to prove a real, unique human without the weight of a full identity check.
## In Wes's words
A named market-research founder on why fraud is inevitable once money is involved, and why a real verification changes the game. Tap play on either clip to hear him.
The half a worm: the fraud you do not catch until a decision has already been made on the data.
A whole different ballgame: a verified human versus a random signup with a made-up name.
## Questions research teams ask
A check turns some traffic away, but look at what it turns away: bots, duplicates, and survey farms that were never going to be quality completes. Real people clear the scan in seconds, a returning respondent is recognized without doing it again, and how strict it runs is tuned to your study. It is a lower cost per quality response, not just friction.
Keep them, they are cheap and worth running. But passive signals are easy to fake, and AI is now better at looking human than many people are, which is why teams come to us after the passive approach stops holding up. VerifyYou sits behind your existing checks, for the abuse that now clears them.
Place the check right before the incentive, so real people push through. Screen-outs are routed back to your panel's disqualification path, so the exchange sees a clean end state and keeps sending traffic rather than stalling the audience. The success and screen-out redirects are both part of setup.
Your systems receive a verdict, never a face. The selfie becomes a face vector, a numerical representation of the face's geometry, not a photo, and we do not store images. No name, no government ID, and no extra PII unless you explicitly turn identity sharing on.
Pricing depends on your volume and your use case, so there is no single number to quote here. Tell us what you are protecting and roughly how much traffic you expect, and we will size it with you. [Book a chat](https://sales.verifyyou.com/) for a quote.
Yes. [Try the demo](https://app.verifyyou.com/verification/PsvfRgX5SEGeVMB_Prv2Aw) right now, no signup and no integration, exactly what your respondents would go through. When you are ready, the next step is usually a free pilot with a branded link you push to a batch of your own respondents.
## Where to go next
The plain explainer: the one question it answers, the two things it proves, and what it is not.
How much of a panel is really bots, duplicates, and survey farms, in third-party figures.
Liveness and uniqueness in a few seconds, and the honest boundary.
The real screens a respondent sees, click to complete, and the demo.
The Human Data Premium, the longer argument, with the charts and citations.