Double your niche effective sample size

Boost your data with precise and coherent AI-powered predictions.
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More granular insights. Less data.

Unveil insights into niches considered once too hard or expensive to reach

Train a deep machine learning model per dataset uploaded

Statistical AI learns patterns across segments
Training relies solely on the uploaded dataset

Boost a niche of interest with predictive respondents

Uses training from adjacent segments to extrapolate
Generates new, original, synthetic respondents

Double the effective sample size of your niche

Statistically equivalent to twice the amount of real respondents
Validated through parallel testing

Simple workflow, solid reliability

Book some time to talk to one of our specialists and get any question you may have answered.
Schedule a call

Frequently asked questions

Can we trust synthetic data?
Absolutely - and we can prove it.

Fairgen's pioneer data augmentation solution can generate near-real synthetic results into niche segments. As long as based on a model trained on your general audience survey.

Technical validation is a key part of every workflow in Fairgen.
Can anyone sign up?
Currently, any organization can apply online by scheduling an introductory call with our team. We are working on a Public Beta that will be open to our community. Be among the first to get access by joining our waitlist.
What is the impact of Fairboost on Insights teams?
Insights teams can immediately expand coverage across niche markets, while gaining fast access do data and without the need of costly traditional data collection boosts.

Analysts can significantly reduce time-to-insights when it comes to niche markets, speeding insights delivery to its stakeholders and ultimately accelerating business outcomes.

Access to once "impossible-to-reach" segments is an added value: by extrapolating results while maintaining high confidence levels, niche segments once considered too small, or too hard-to-reach for fieldwork boosts, can now be analyzed.
How is the technical validation process for Fairboost?
In short, you will run several parallel testing processes that will compare the efficiency of synthetic data versus real data.

The primary method used by our clients is "Backtesting", using as a base existing datasets. By the end of it, our predictive technology's accuracy and coherence both on aggregated and granular results can be determined.

The process is simple: you will provide a fraction of an existing dataset for Fairgen to be trained on, so we can mimic a situation where you have scarce data. Let's say 10% of it. The remaining of it will be used as the ground truth and Fairgen will generate a synthetic dataset that was trained on only 10% of it. We will then compare its reliability against samples of the "ground truth".

We guarantee a reliable and coherent augmentation that is worth 2x of any niche segment within your original dataset.
Can Fairgen handle any type of data?
Yes. Fairgen is equipped to handle any quantitative (close-ended) data points that can be exported in a columnar format (single and multiple choice questions, rankings, NPS, continuous open-ended, ratings, and more). Fairgen automatically recognizes field types and conditional relationships between questions. 
Can Fairgen augment qualitative questions?
No. Fairgen’s can augment only quantitative fields. We are however working on that, and it should appear in future Fairgen releases! 
How is it different than reweighting?
In a nutshell, Fairgen will reduce your niche segment's margin of error while classic reweighting techniques will not.

It's common for research firms to, behind the scenes, apply different weights for specific segments in a quantitative surveys when the data collected do not perfectly reflect the demographics or characteristics of the overall population.

When this is done, there is no impact on margins of errors.

Fairgen, on the other hand, can reliably reduce margins of errors, achieving the same performance as twice the sample size.