Transform small-scale surveys into population-representative datasets using advanced generative AI. Bridge the gap between rich attitudinal research and large representative datasets — without compromising statistical rigor.
Population-representative research takes 3-6 months and strains budgets. Sample size and coverage are often compromised.
Affordable n=1,000 studies provide rich insights but can't support granular demographic analysis or population-level projections.
MAPS and AMPS provide scale but you can't add your custom brand questions. You're limited to what's already in the questionnaire.
Statistical matching and imputation methods don't preserve complex multivariate relationships between attitudes and behaviours.
AI-powered scaling that preserves your survey's richness while achieving population representativeness
Maintain complex attitudinal and behavioural patterns from your original research
Generate data conditioned on real demographic distributions for true representativeness
Project n=500-2,000 surveys onto n=10,000-50,000+ population datasets
Automated quality metrics ensure statistical accuracy with transparent grading
From survey data to population-level insights in four steps
Survey data (n=500-2,000) and population universe (n=10,000+). Supports CSV, SPSS, Excel.
Select hook variables (demographics) and target variables (attitudes, behaviours to project).
AI learns patterns from survey, generates synthetic respondents matching population demographics.
Automated quality metrics, variable-level accuracy scores, export with full validation report.
Where Synthetic Projection creates value
Project brand awareness, perception, and purchase intent from small surveys onto full population datasets for granular demographic analysis.
Enhance CRM data with attitudinal variables, creating rich psychographic segments at population scale.
Estimate total addressable market (TAM) for new products by projecting adoption patterns to national populations.
Test product concepts with small samples, then project appeal and purchase intent across full market.
Identify motivational drivers from small samples, then project psychological profiles across your customer base for personalized engagement strategies.
Run brand resonance measurement on small samples, project to full market for competitive landscape analysis.
Every projection includes comprehensive validation metrics
| Quality Grade | KL Divergence | Recommended Use |
|---|---|---|
| A+ / A | < 0.10 | Full analytical use — high confidence |
| B+ | 0.10 - 0.15 | Most analytical applications |
| B | 0.15 - 0.20 | Directional insights |
| C | 0.20 - 0.30 | Exploratory analysis only |
| D / F | > 0.30 | Do not use |
Input quality validation — Sample size, coverage, missing data checks
Distributional accuracy — Statistical similarity to source survey
Variable-level performance — Individual quality scores per variable
Segment-level plausibility — Logical patterns within demographics
Why Synthetic Projection outperforms legacy approaches
| Method | Limitation | Synthetic Projection |
|---|---|---|
| Imputation | ✕ Fills gaps, doesn't scale | ✓ Generates new respondents at population level |
| Reweighting | ✕ Extreme weights distort analysis | ✓ No weighting issues, natural scaling |
| Statistical Matching | ✕ Doesn't preserve joint distributions | ✓ Maintains complex multivariate relationships |
| Full Population Survey | ✕ 3-6 months, major budget commitment | ✓ 2-3 weeks, fraction of the investment |
Transform small-scale research into population-level intelligence — in days, not months.
Explore Synthetic Projection