Sample microdata
- Useful records, limited density
- Rare segments disappear quickly
- Coarse geography and access limits
- Hard to stress-test local scenarios
- Often disconnected from homes, anchors, and mobility context
Synthestat population infrastructure
Hyper-realistic synthetic populations at local regional national continental scale
We build 1:1 synthetic populations that reproduce real demographic, household, spatial, economic, and behavioral structure without exposing real people.
1:1
population scale
EU
country-by-country buildout
QA
validation visible by release
Beyond microdata
Samples were the ceiling. Full synthetic populations are the new substrate.
Sampled microdata changed what analysts could do. But enterprise decisions now need local coverage, rare-segment stability, spatial context, scenario testing, and privacy controls that small samples cannot reliably provide.
Synthestat substrate
- Full synthetic population coverage
- Local and segment-level representativeness
- Privacy-preserving artificial entities
- Rerunnable releases for scenario and change analysis
- People, households, homes, workplaces, and anchors in one coherent world
What we build
Not a file. A behavior-ready artificial population.
Synthestat turns population evidence into a model-ready world: synthetic people inside households, assigned to places, enriched with attributes, and connected to the activity anchors enterprise models actually need.
Persons and households
Age, sex, household structure, family roles, education, labour, income, and tenure.
Homes and geography
Residential placement that supports fine local analysis where evidence permits.
Work, school, and services
Activity anchors that make the population usable for planning, mobility, and demand models.
Audience and behavior context
Segments that carry demographic, spatial, economic, and modeled behavioral signals together.
Why buyers can trust it
Quality discipline that is visible, measured, and release-aware.
Synthestat is designed for serious use: official statistics provide the backbone, synthetic outputs fit marginals and distributions, and quality is evaluated through validation gates, tolerance bands, evidence lineage, and governance labels.
Enterprise applications
Where sampled microdata stops, a full synthetic population keeps working.
Market research
Build synthetic audience segments that stay coherent across demographics, location, household context, and modeled behavior.
OOH and media
Measure reach, frequency, exposure, and scenario changes against a full population rather than a thin respondent panel.
Retail planning
Understand catchments, demand pockets, and local household structure before committing capital.
Transport and mobility
Connect homes, workplaces, schools, and services to model movement pressure and disruption effects.
Insurance and risk
Estimate local exposure with synthetic households and assets while keeping real individuals out of the workflow.
Public planning
Evaluate policy, infrastructure, and service scenarios with a population surface that can be audited.
Privacy and governance
Synthetic by design. Auditable by default.
Synthestat entities are artificial. They are built for analysis, simulation, and decision support without handing teams direct access to real person records.
No synthetic person is presented as a real individual.
Exports can be controlled, aggregated, and scoped to the buyer's need.
Evidence lineage, release labels, and validation reports support review.
Proof surface
Inspect what is ready, partial, and under validation.
Europe-scale coverage is being built country by country, with QA surfaces designed to show readiness, quality ribbons, validation posture, and release progress instead of hiding uncertainty.
Open qa.synthestat.com
Start with the hard question
Bring us the population question your current microdata cannot answer.
We will help you decide whether a full synthetic population is the right substrate for your market, media, mobility, risk, or planning workflow.