Stop Sophisticated Agentic Fraud
Sophisticated agentic fraud is the fastest growing threat to research quality.
Agentic bots now complete entire surveys autonomously, and respondents use ChatGPT, Claude, and other LLMs to generate plausible open-ends in seconds. These responses pass basic quality checks because they are well-written, on-topic, and grammatically correct. That is exactly what makes them dangerous. Rep Data stops them on two fronts. Our pre-survey fraud prevention blocks automated agents before they enter your survey, and our in-survey data cleaning scores every response to catch anything that slips through. Twelve detection methods across the full survey lifecycle.
The Problem
Why AI responses are uniquely dangerous
Unlike traditional fraud, AI-generated responses look thoughtful, well-written, and on-topic. That makes them harder to catch and more damaging to your insights than obvious bad data.
Plausible but Fabricated
LLMs generate coherent, grammatically correct responses that sound like real opinions. They pass spelling checks, length filters, and basic relevance screening with ease. Traditional quality tools were not built to detect this.
Synthetic Consensus
AI responses tend toward generic, middle-of-the-road opinions. They create false consensus in your data, masking the real variation in attitudes that you are trying to capture. Your insights end up reflecting an LLM, not your audience.
Agentic Fraud at Scale
We are past individual respondents pasting ChatGPT answers. Agentic bots now complete entire surveys autonomously, using automated drivers, virtual machines, and programmatic browsing. One bad actor can generate hundreds of plausible completes per hour. This problem is accelerating.
Impact
What AI contamination does to your data
Invisible
AI responses pass traditional quality checks designed for human fraud
Growing
AI-generated responses are the fastest-growing fraud vector in research
Biased
LLMs produce systematically biased opinions that skew your findings
Scalable
Agentic bots complete entire surveys autonomously at industrial scale
The Solution
How Rep Data detects and blocks agentic fraud
We regularly identify new ways to isolate and block AI-driven fraud, so you get cleaner data. Here are six categories covering 12 specific detection methods we use today.
LLM Device and Signal Detection
We detect explicit LLM device signals and identify operator device types signaled by LLM providers. When a respondent's session originates from an AI agent or automated pipeline, we catch the technical fingerprints that give it away before the survey even begins.
AI Language Pattern Detection
Our in-survey data cleaning's Open-Ended Score uses model-based analysis to detect LLM-generated text by examining linguistic patterns, syntax, word choice, and response structure. It distinguishes machine-written answers from authentic human responses, catching outputs from ChatGPT, Claude, and other models.
Automation and Script Detection
Detects automated drivers and scripts like Selenium that agentic bots use to navigate surveys. Identifies virtual machines by comparing advertised system attributes to actual machine behavior via JavaScript. Catches the infrastructure that powers automated survey completion.
Behavioral Biometrics
Detects non-human mouse movement patterns, programmatic typing behavior, and copy-paste input mechanisms in real time. Human respondents produce natural variation in their interactions. Bots and AI agents produce patterns that are measurably different, and we catch that difference.
Programmatic Browsing and Crawler Detection
Detects programmatic browsing behavior and known crawler devices. Uses statistical inference to identify suspicious user-agents and machines that do not match the profile of legitimate survey respondents. Catches the automated tools that agentic fraud relies on.
Cross-Ecosystem Hyperactivity
With 5 billion annual scans, pre-survey fraud prevention sees when an agent or respondent is moving across the survey ecosystem at inhuman speed. Hyperactivity detection assumes the agent moves across multiple surveys and panels, catching behavior that single-survey checks miss entirely.
Defense in Depth
A complete quality stack, not a single tool
- Pre-survey fraud prevention detects LLM device signals, automated drivers, virtual machines, and suspicious user-agents before entry
- In-survey data cleaning scores every response across coherence, behavioral biometrics, open-end quality, speed, and engagement
- AI language detection uses model-based analysis to identify machine-generated text across all open-ended responses
- Behavioral biometrics catch non-human mouse movement, programmatic typing, and copy-paste input in real time
- Cross-ecosystem hyperactivity detection catches agents moving across the survey ecosystem at scale
- Continuously updated detection methods adapt as new AI capabilities and agentic tools emerge
Client Results
Trusted by leading research teams
“Speed and flexibility are key in our research projects. With [Rep Data], the building, fielding, and analysis can literally all be done in a matter of days, and the easy-to-understand charts can be accessed at the touch of a button.”
Joanne
Senior Project Director, TCC Global
“We trust that the data Rep Data is giving us is clean, real and representative. I even recommended their services to my previous employer.”
Ben Winston
Senior Director, ClearPath Strategies
“We did a couple of tests and the data was so much better that we were instantly sold. They're now our primary provider.”
Scott Rasmussen
Founder, RMG Research