15+ detection layers, before the questionnaire
Device, network, identity and behavioural signals are evaluated in the same request that admits the respondent, so a blocked entrant never consumes a quota cell or generates a record to clean up later.
Use cases · SurveyGuard
Stop fraud at the door, not at reconciliation
Where a client-side insights team starts, because their name is on the data’s integrity. Fraud concentrates precisely where completes are most expensive, which means the audience you worked hardest to reach is the one most worth faking.
Three products, one job
- Open NetworkReach the audience no single panel can fill
- FieldworkOSRun every supplier from one screen
- SurveyGuardStop fraud at the door, not at reconciliation
All three work alone. Which one you reach for first depends on who you are and what is breaking.
The problem it solves
Cleaning finds the damage
after you have paid for it.
Between 10 and 30 percent of online survey responses are fraudulent when no real-time protection is in place. That is up to a third of the data informing product launches, medical research and public policy.
And the incentive is worst exactly where it hurts most. A high-CPI specialist complete attracts professional respondents, misrepresented qualifications and bots that a cheap general-population complete never would. The harder the audience, the stronger the reason to pretend to be it.
Post-hoc cleaning is the usual defence, and it arrives late by construction. By the time a record is flagged in tabulation you have already paid the CPI, already burned the quota cell, and quite possibly already shipped the topline.
Every respondent, at the door
<200ms · 80% of traffic- Allow0–29All checks passed. Zero friction for legitimate respondents.
- Flag30–59Suspicious patterns. Manual review recommended.
- Challenge60–79Additional verification required before proceeding.
- Block80–89High-risk respondent blocked from survey access.
- Ghost90–100Professional fraudster silently fed a decoy survey.
- ReviewAnomalyHardware passed, but behaviour warrants human review.
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One unified risk score, six verdicts, decided in the same request that admits the respondent rather than in a cleaning pass weeks later.
What SurveyGuard does
Evaluated at the entry point, in the same request that admits them.
SurveyGuard sits at the door of every survey. Each respondent is scored across 15+ detection layers and given one of six verdicts before they reach your questionnaire, with under 200ms response for 80% of traffic and no measurable friction for real respondents.
Duplicates that clearing cookies will not solve
Hardware, browser and behavioural fingerprints link the same person across accounts and sessions. A professional respondent swapping identities is caught by what the device and the typing look like, not just by IP.
A reputation graph across the whole ecosystem
Every respondent is checked against a cross-agency global reputation database, so a fraud network burned on someone else's study arrives at yours already known.
Ghost Protocol for professional fraudsters
The highest-risk profiles are silently fed a decoy survey rather than a rejection, which keeps them from learning the detection boundary, and feeds their signature back into the global reputation system.
Accuracy that does not tax real respondents
95%+ detection accuracy with under 1% false positives, and a green-lane response under 200ms for 80% of traffic, so legitimate respondents notice nothing at all.
Attribution, and a record of it
Every verdict is attributed to the supplier who delivered the respondent, which turns quality into a live per-supplier number rather than an argument at reconciliation.
Detection is probabilistic, and we say so in the terms: no system detects every fraudulent respondent. What changes here is where the decision happens. Prevention at the door rather than cleaning at the end, with 95%+ detection accuracy and under 1% false positives on the traffic it scores.
Use cases by team
Where prevention pays for itself.
Agencies feel fraud as completes they paid for twice. Suppliers feel it as their own brand carrying a partner's problem. Insights teams feel it as a number they acted on that was never real.
Protect the high-CPI audience
On a specialist B2B or healthcare study, each complete is expensive and fraud concentrates there for exactly that reason. Blocking a bad respondent at the door means you never pay for a fabricated complete, and never ship one.
Kill duplicates that evade cookies
A professional respondent clearing cookies and swapping accounts defeats IP-level checks by design. Hardware and behavioural fingerprinting is what catches the same person arriving for the fourth time.
Quality per supplier, while field is open
Because verdicts are attributed to source, a supplier whose traffic fails at three times the rate of the others is visible on day one, in time to pause them and reallocate rather than to write it up afterwards.
Prove authenticity for a specialist claim
A “cardiologist” complete at specialist CPI attracts exactly the fraud a cheap general-population complete does not. Checks at the door and a cross-ecosystem reputation graph are what keep the misrepresented respondent out of a dataset that will face medical or regulatory review.
Keep partner-sourced respondents clean
When a study runs past your own panel into partners, fraud entering through a partner is still fraud with your name on it. Scoring at the door means it never reaches your client's dataset, and the per-partner tiering you promised is enforced while field is open.
Enterprise-grade quality with no team
A solo consultant can hand a client the same fraud assurance a large agency gives, because the checks run at the door regardless of how many people are behind the study.
Data you can certify
An insights team can show a board or a regulator where fraud prevention happened and what it caught, because the decision was made and logged at the entry point rather than inferred later from a cleaning script.
Integrity that is not for sale
Detection thresholds do not soften for larger accounts. It is one of our stated operating principles: the moment fraud protection becomes negotiable, it becomes worthless. For a client-side team, that is the assurance that the number they are acting on was not compromised by someone else's commercial pressure.
The outcome
Integrity becomes a property of collection.
Data quality stops being a cleanup step performed on a finished dataset and becomes a condition of entry to it. The decision moves to the door, before the record is written, before the CPI is owed, and before a quota cell you cannot easily re-open has been spent on someone who was never eligible.
Pairs with
Three products, one study.
Protection at the door is strongest when the supply was named to begin with and the operation behind it was run honestly, because then a verdict has a supplier attached and a consequence available. The worked example follows a single German healthcare study through all three, from a 320 shortfall to a reconciled invoice.
Which product first
All three work alone. Who you are decides where you start.
Together they cover sourcing a study, running it, and protecting what comes back. Nothing here has to be adopted in order.
| If you are | Start with | Because |
|---|---|---|
| Client-side insights | SurveyGuard | Your name is on the data's integrity. |
| Full-service agency | FieldworkOS | Coordination is your daily bottleneck. |
| Independent researcher | FieldworkOS | It is the operations team you do not have. |
| Sample supplier | FieldworkOS | Your partner layer needs a system. |
| Healthcare / specialist | Open Network | Reaching the audience is the hard part. |
Want to see all three on one study rather than product by product? The hard-to-reach sample walkthrough follows a single German healthcare study through six steps, from the shortfall to the reconciled invoice.
See it on your own work
Point it at the study you trust least.
The most useful evaluation is on live traffic to a high-CPI audience, where fraud has the strongest reason to show up. We will walk through what gets scored, what gets blocked, and what your own data says.