01
Research quality is an integrity problem
When respondents use AI to fabricate answers, rotate identities, or rush through surveys, traditional attention checks are not enough. Straight-lining, speeding, and trap questions catch some noise-but they were not designed for synthetic respondents who can generate fluent, on-topic text at scale. Honrly adds higher-context screening for modern fraud patterns so your sample reflects real people, not automated or duplicated behavior.
- Flag duplicate and synthetic behavior patterns before they enter analysis
- Reduce spend on low-quality completes that inflate quotas without adding signal
- Protect longitudinal and high-stakes research programs where contamination compounds over time
- Give researchers a reviewable basis for keep, recontact, or discard decisions
02
Where research fraud shows up
Fraud is not limited to cheap consumer panels. Academic studies, UX research, clinical screening surveys, brand trackers, and B2B insight programs all attract bots, professional survey-takers, and AI-assisted respondents. The cost is not only wasted incentives-it is biased findings, failed replications, and decisions made on contaminated data.
- Duplicate identities and device/session reuse across studies
- AI-written open ends that look fluent but lack lived specificity
- Inattentive or scripted completes that pass basic quality gates
- Panel farms and coordinated response patterns that skew segments
03
Evidence your team can trust
Instead of opaque quality scores alone, Honrly supports review with structured signals so researchers can decide what to keep, recontact, or discard. Integrity in research should be explainable to stakeholders who fund the study and to teams who will act on the results. A black-box “bad respondent” label is not enough when sample quality is contested.
- Structured signals that support human review, not silent auto-deletion by default
- Clearer separation between low-quality noise and high-confidence fraud patterns
- Audit-friendly context for agencies, insights teams, and academic PIs
04
Works alongside your survey stack
Honrly is an integrity layer-not a replacement for Qualtrics, SurveyMonkey, panel providers, or custom research platforms. Teams keep their instruments, incentives, and analysis workflows. What changes is the ability to defend respondent authenticity before analysis locks in bad data.