Deeper cuts and cleaner data from the studies you already run
Boost under-sampled segments with predictive respondents, and catch bad data before it reaches your report.
Fairgen Boost
Read hard-to-reach segments without extra fieldwork
Boost generates predictive respondents from the patterns in your own study, so segments that were too small to report become readable. Models are trained only on each study’s data.
You choose where to boost
Pick the under-sampled segments that matter and validate them with side-by-side comparisons.
Respects the questionnaire
Understands skip logic, piping, multi-selects and hidden variables, not just data points.
Error margins like tripling the sample
Boosted segments reach error margins comparable to three times the real sample size.
Benchmark at scale
Tested thousands of times, not once
Scale
10,000+ boosts
Validated across more than ten thousand boosts.
Coverage
B2B, B2C, opinion and healthcare
Proven across study types, not a single category.
Proof
Customer stories
How Ifop, L’Oréal, T-Mobile and Accor read segments that were too small before.
Learn more →
Where firms use it
Methods that gain the most
Tracking
Trackers
Keep wave-on-wave reads stable for small subgroups without inflating sample.
Segmentation
Segmentation and U&A
Size and profile segments that would otherwise be too thin to report.
Opinion
Opinion polling
Read regional and demographic cuts with tighter margins, without extra fieldwork.
Post-tests
Post-tests and ad testing
Fill gaps in hard-to-reach groups and see results across every segment.
Fairgen Check
Catch bad data before it skews your results
Check is the last line of defense against low-quality respondents: pre-built tests and AI agents that flag speeders, straightliners and suspicious answers in minutes.
Instant scorecards
Optimized tests across closed and open-ended questions in a first pass.
Agentic checks
AI agents surface contextual inconsistencies that rules miss, following your own logic.
A report you can share
A clean dataset and a QA report with flagged respondent IDs and reasons.
How Check works
From survey export to a clean dataset
Step 01 · Upload
Upload
Send your survey export as .sav, .xls, .parquet or .csv.
Step 02 · Outliers
Outliers
Automated detectors for speed, duplicates, patterns and open-ended quality.
Step 03 · Agentic
Agentic
AI agents replicate human review at the row level, including contextual inconsistencies.
Step 04 · Export
Export
A clean dataset and a shareable QA report with flagged IDs and reasons.
“Fairgen Check has enabled us to build faster, more proactive processes to verify data quality in real time, whatever the source, and take corrective action to ensure more reliable insights.”
Thomas Duhard
Head of Data Projects, IFOP
FAQ
