Honrly
HONRLY

Use case · Research

Integrity for research panels and surveys

Bots, duplicate participants, and AI-fabricated open responses poison research. Honrly helps research teams detect inauthentic participation and review evidence before insights ship to product decisions.

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01

How research gets poisoned

Incentive-driven panels attract fraud. Generative AI makes open-text responses look human in seconds. Classic attention checks and speed traps catch some bots-and miss sophisticated AI-fabricated answers that pass those filters.

02

Fraud patterns researchers actually see

Duplicate accounts across studies, copy-paste open responses, suspiciously generic “user stories,” and participants who cannot defend their own answers in a follow-up. Integrity programs need signals for those patterns-not only a CAPTCHA at the start.

03

What Honrly adds to panel workflows

Integrity signals, review workflows, and evidence researchers can inspect-without pretending every flag is proven fraud. Researchers decide exclusions and document rationale so product partners trust the cleaned dataset.

Workflow

How it works

Select competencies → generate questions → score evidence → verify integrity

Assessment loop
Select
Role · Customer Success Manager
Customer empathy
30%
Ownership
25%
Communication
20%
Adaptability
15%
Conflict resolution
10%

Integrity gate

Threats get intercepted. Genuine candidates pass through.

A four-scene integrity loop: live session, anomaly detection, layered interception, and verified pass—built for AI-era cheating.

  • Device & session
  • Behavior & language
  • AI assistance
Honrly integrity
Session live
Candidate sessionLive

“When a renewal was at risk, I called the customer the same day and owned the save through Q3…”

Response · 00:14:22
Integrity watch
  • Identity checkok
  • Device postureok
  • Response cadenceok

Hiring pipeline

Without a gate, risk flows into every hire. With Honrly, it doesn't.

Watch one hiring wave: intake, unchecked advance, gated interception, then a shortlist built only from verified sessions.

Candidate stream
Intake
Hiring wave · CSM6 sessions
A. Chen
Ownership 5
In queue
R. Okonkwo
AI assistance
In queue
M. Patel
Judgment 4
In queue
J. Rivera
Hidden overlay
In queue
S. Kim
Evidence 5
In queue
T. Brooks
Paste burst
In queue

Evidence report

Excerpts hiring panels can actually use

Each competency surfaces quoted evidence, rubric anchors, and an integrity status—not a vague fit number.

Candidate report
Ownership
Ownership5 / 5

“I owned the renewal risk, called the customer the same day, and tracked the save through Q3.”

Same-day actionNamed accountabilityFollow-through

04

Keep researchers in control

Auto-dropping every flagged participant can bias samples. Review queues, clear exclusion rules, and participant-facing policy protect both data quality and ethics. Protect participant experience with clear rules before the study starts.

05

Product deep dive

See Research Integrity for the full product narrative. This use case page is for UX, insights, and market research teams evaluating panel and survey authenticity specifically.

FAQ

Frequently asked questions

No. It adds an integrity layer around panel and survey quality while you keep your recruiting sources.

Related

Keep exploring

Next step

Ready to clean bots and AI fabrication from your panels?