The market research industry is fighting a quiet but escalating battle: data fraud. As respondent quality drops and AI-generated noise creeps into surveys at scale, insights teams are spending more time screening responses than analyzing them. Fairgen, best known for its synthetic data technology used by brands including L’Oréal, T-Mobile, YouGov, and Cint, believes the fix lies in automated, post-collection verification.

The company today introduced Fairgen Check, a standalone assurance layer designed to review datasets after fieldwork and catch issues that legacy checks routinely miss. While pre-survey filters and in-field monitors have become common, the final stage of quality review still falls on manual judgment. That bottleneck is precisely what Fairgen aims to eliminate.

A New Safety Net for Post-Collection Data

Fairgen Check operates as an independent gatekeeper once data collection wraps. It scans any dataset through a system blending statistical anomaly detection with advanced language models. The goal is simple: ensure only coherent, trustworthy responses make it into the final analysis.

Fernando Zatz, Fairgen’s Chief Product Officer, said research teams are encountering “an unprecedented surge in low-quality participation.” Despite improvements in early-stage checks, post-hoc reviews remain inconsistent and time-consuming. Fairgen Check, he argued, offers a standardized and objective layer of evaluation that levels the playing field.

This is increasingly vital. As survey fraud becomes more sophisticated and low-effort participation increases across global panels, insights teams risk basing multimillion-dollar decisions on compromised data. Competitors have started experimenting with machine-learning filters, but many tools still require substantial human validation. Fairgen is positioning Check as the missing link between automation and reliability.

Three Layers of AI-Powered Quality Control

To separate useful data from noise, Fairgen Check evaluates responses across three integrated layers:

1. Statistical and Behavioral Screening
The first layer flags erratic response patterns, speeders, and straightliners. These checks mirror what many insights teams do manually, yet Fairgen applies them consistently across entire datasets. This ensures anomalies are surfaced even when large-scale studies bury them in volume.

2. Open-Ended Response Integrity
The second layer reviews qualitative inputs. It identifies irrelevance, duplication, gibberish, and—critically—AI-generated text. With ChatGPT-style tools now used by bad actors to mass-produce open-ended responses, this detection step has become essential.

3. AI-Based Questionnaire Inspector
The final layer introduces an agentic, GPT-like quality inspector. Unlike traditional filters, this model understands the intent behind each questionnaire, interprets context, and points out contradictions that even seasoned reviewers might miss. It behaves like a meticulous analyst who never gets tired, distracted, or inconsistent.

Together, these layers create a scalable, transparent system that minimizes subjective decision-making. The promise is a faster, more objective route to trustworthy data.

Early Users See Efficiency Gains

Ifop, one of Europe’s leading research firms, is among the early adopters. Thomas Duhard, Head of Data Projects, said Fairgen Check helped the team “build faster, more proactive processes to verify data quality in real time.” He noted that corrective actions now happen earlier, which protects insights from being compromised downstream.

This isn’t just about speed. It’s about restoring confidence in datasets that play a major role in marketing strategies, product design, and customer experience programs. The industry has struggled to keep up with evolving fraud patterns, and Fairgen’s approach suggests that automated, post-collection oversight is no longer optional.

Fairgen Expands From Tech Provider to Full Platform

For Fairgen, the launch represents more than a new product. CEO and Co-Founder Samuel Cohen described Check as part of a broader evolution toward becoming a full AI platform for researchers.

“The launch of Check is another step toward fulfilling our mission: empowering researchers to democratize research by augmenting human expertise with trusted AI,” Cohen said. He indicated that more releases are on the way, hinting at a platform strategy that mirrors the expansion paths of analytics players like Qualtrics and Momentive in earlier eras.

Yet Fairgen stands apart by rooting its innovation in synthetic data and advanced generative AI—two areas reshaping the future of research. As panel quality continues to decline, tools that diagnose, repair, and supplement human-gathered data will become essential infrastructure.

Why It Matters

The broader implication is clear: the research industry is being forced to modernize its quality stack. Fraud has scaled beyond what internal teams can manually handle. Fairgen Check offers a compelling glimpse at how automation, statistical rigor, and linguistic intelligence can converge to protect insights from contamination.

As market research firms and enterprise insights teams push for faster turnarounds and global reach, the need for automated quality systems will only grow. Fairgen appears ready to fill that gap, and Fairgen Check may become one of the baseline tools researchers rely on to restore trust in the datasets that drive modern business decisions.