Emporia stops AI respondents by screening every participant before a study starts. Our anti-fraud engine, Pori, checks each person's professional identity, their device and network and the quality of what they write. Anyone who fails is filtered out before fieldwork begins, so AI-generated answers never reach your dataset.

Think you could catch an AI respondent yourself? Play Are You For Real? below. You will see three answers to the same question. Two came from verified professionals and one came from AI. Pick the AI.

Why are AI respondents a problem for B2B research?

AI respondents are a problem in B2B research because they produce answers that look reasonable and carry no real experience behind them. A language model can write a fluent paragraph about reviewing production bids or handling a security incident without ever having done either. When those answers enter a B2B dataset, they blend in with real responses and shift the findings in ways that are hard to detect after the fact.

B2B research is hit harder than consumer research for three reasons. Sample sizes are smaller, so each fake response carries more weight. Incentives are higher, which makes professional surveys a target for fraud farms. Screeners ask about job titles and responsibilities that a fake profile can be built to match.

The cost shows up after fieldwork closes. Teams spend time cleaning data by hand, rerun studies that came back unreliable and make decisions on findings that a handful of synthetic responses have skewed.

What does Emporia's anti-fraud engine check?

Emporia's anti-fraud engine checks three things about every respondent: who they are, where they are connecting from and how they answer. The engine is called Pori. It was built for B2B research and it screens both Emporia's first-party community, Polis, and the third-party sources it taps through Levada.

Professional identity

The first layer confirms the person is a real professional at a real company. Pori analyzes the respondent's work email domain to confirm it belongs to an established company. It checks the respondent's stated role and experience against live LinkedIn profile data. It also matches each respondent against Emporia's records of past and flagged identities.

Technical and network signals

The second layer looks at the device and the connection. Device fingerprint analysis catches people who create many accounts from one machine. VPN, proxy and Tor detection flags the hidden connections that fraud farms typically rely on. Synthetic identity detection spots AI-generated profiles and fake personas built to slip through screeners.

Response quality

The third layer reads the answers themselves. Sentiment and quality analysis reviews open-ended responses to catch gibberish, bots and low-quality replies. Response pattern monitoring watches for rushed answers, straight-lining, copied and pasted responses and inconsistencies across a survey. Cases the system cannot settle on its own go to Emporia's sampling experts for human review.

What happens when a respondent is flagged?

A flagged respondent is filtered out before your study begins. Screening happens at the point of entry, not after fieldwork closes, so a flagged person never receives your survey and never contributes a response you have to remove later. Their identity is added to Emporia's records of flagged identities, which strengthens the data lake lookup for every study that follows.

Members of Polis are verified on a recurring basis, not once at sign-up. A profile that passed last year is checked again, which matters because fraud tactics change and a real account can be sold or shared.

The result is a study that starts with 100% verified respondents. You spend fieldwork time on analysis instead of cleaning.

Why is identity verification alone not enough?

Identity verification alone is not enough because a real person with a real LinkedIn profile can still submit AI-written answers. Confirming that someone exists tells you nothing about whether they wrote what they submitted. That is why Pori pairs identity checks with network detection and response analysis. A respondent has to be a real professional, connecting from a real device, giving answers a real professional would give.

The game above is built on that third check. The professionals whose answers appear in it were all verified through Polis. The AI answers were generated for the game and would never have made it through screening, but they show how convincing a synthetic response can look on its own.

Frequently asked questions

How does Emporia verify that a survey respondent is a real professional?

Emporia confirms the respondent's work email belongs to an established company, checks their claims against live LinkedIn profile data and matches them against its records of past and flagged identities.

Can AI-generated survey responses be detected?

Yes. Emporia's Pori engine reads open-ended answers for gibberish, bot patterns and low-quality replies, monitors for rushed answers, straight-lining and pasting responses, and detects synthetic identities and AI-generated profiles at the account level.

What is Pori?

Pori is Emporia's proprietary anti-fraud technology. It screens every respondent from Emporia's first-party community, Polis, and from third-party sources accessed through Levada before a study begins.

Does Emporia use human review?

Yes. Cases the automated checks cannot settle are sent to Emporia's sampling experts for human judgment.

Start your next study with verified respondents

AI respondents are getting harder to spot by eye, and the game above shows why. Emporia removes the guesswork by verifying identity, network and response quality for every participant before fieldwork starts. Learn more about Emporia's anti-fraud technology or talk to our team to start your next B2B study with 100% verified respondents.