Pre-field audience estimation
Ask a calibrated model of the United States
HiveSight estimates how a defined population — national, state, congressional district, or segment — would answer a survey question. It elicits response distributions over census-calibrated population cells and aggregates them with real weights. A directional prior before you field, not a replacement for polling.
1
Partition
Your audience is resolved against a calibrated synthetic population built from Census Bureau microdata — 4.2 million records with state and district assignment — and partitioned into ~150 weighted cells: age, earned income, sex, housing, children, benefits.
2
Elicit
For each cell, the model estimates the full response distribution directly — the approach that beats one-call-per-fake-respondent roleplay in head-to-head research, at a fraction of the cost.
3
Weight
Cell distributions aggregate with calibrated population weights, the same post-stratification logic survey statisticians use. Every run records its dataset, prompts, model, and seed.
Measured, not promised
Benchmarked against real survey data — misses included
Mean absolute error against human targets from GSS 2024 and SHED 2024 — 63 questions, pre-registered before any model runs. Lower is better. Persona roleplay, the standard synthetic-respondent architecture, is not competitive. Cells match direct estimation on margins and beat it on subgroup rank structure (Spearman 0.62 vs 0.48) — while staying coherent, composable across any audience filter, and fully auditable. Error is real, which is why every result ships with it attached.
| Method | Topline error | Subgroup error |
|---|---|---|
| Persona roleplay (typical AI-survey approach) | 25 pts | 25 pts |
| Direct model estimate | 8.6 pts | 9.8 pts |
| HiveSight population cells | 9.2 pts | 9.8 pts |