Fairgen

Fairgen is a Data & Analytics tool that empowers researchers with AI and synthetic data to extend surveys and gain deeper insights. It helps detect fraud and track brand equity for more reliable results.

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At a glance

Pricing

Enterprise · Likely Not Free

Free tier

Yes

API

Skill level

Technical

About

What is Fairgen?

Fairgen is an AI and synthetic data research suite designed to empower researchers with tools to extend surveys, boost niche respondents, detect fraud, and track brand equity. The platform allows users to generate niche data for deeper insights, spot and remove low-quality respondents, and fill in missing answers. It offers premium simulated audiences, or "digital twins," that think, feel, and respond like real audiences, enabling faster testing of ideas. Fairgen supports various use cases including tracking, segmentation, understanding underrepresented groups, and post-test analysis. It provides consultancy-grade insights decks and allows users to engage with their audience through chat, delivering actionable insights quickly.

Best used for

Ideal for researchers and data scientists who need to extend survey reach, understand hard-to-reach audiences, and test ideas with simulated respondents. Especially valuable for gaining granular insights and making confident decisions in brand, product, and marketing research.

Common actions

augment survey data

simulate audiences

detect survey fraud

analyze niche segments

generate synthetic data

researchEducationmarketing

Capabilities

Key features

Generate synthetic data
Detect low-quality respondents
Impute missing answers
Create digital twins
Access simulated audiences
Conduct end-to-end studies

Target Audience

data scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Enterprise · Likely Not Free

Contact for Pricing

FAQs

How does Fairgen's synthetic data differ from reweighting techniques?

Fairgen's approach reduces the margin of error for niche segments, which traditional reweighting techniques do not. While reweighting applies different weights to segments, it doesn't impact the confidence intervals for those specific groups, whereas Fairgen aims to provide more reliable and precise data for smaller populations.

Can Fairgen augment qualitative data or only quantitative data?

Fairgen is designed to augment only quantitative (close-ended) data fields. It can handle various quantitative data points such as single and multiple-choice questions, rankings, NPS, continuous open-ended responses, and ratings. The system automatically recognizes field types and conditional relationships within the data.

How is the technical validation process conducted for Fairgen's synthetic data?

New customers are encouraged to run tests evaluating predictive power, survey structure adherence, and workflow integration. Predictive power is assessed by training the system on a small portion of existing data (e.g., 10%) and comparing the generated synthetic dataset to the full sample, ensuring accuracy and consistency at aggregate and segment levels.

What kind of impact can Fairgen have on insights teams?

Fairgen enables insights teams to make better niche go-to-market decisions by providing fast and affordable access to data that was previously difficult or impossible to obtain. It allows researchers to expand coverage across niche markets, gain deeper trust in analyzing specific groups, and access once "impossible-to-reach" segments with high confidence levels.

Is the synthetic data generated by Fairgen trustworthy?

Yes, Fairgen emphasizes that its synthetic data is trustworthy and provides technical validation as proof. The solution generates near-real synthetic results for niche segments, based on models trained on general audience surveys. Technical validation is a key part of every workflow within Fairgen to ensure reliability.

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Business Intelligence Predictive Analytics Data Labeling & Annotation Real-Time Analytics Data Cleaning & Prep Data Pipelines & Integration

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Research & Education › Academic Research

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