AuraOne

Synthetic Populations & Research Lab

Instant market research on synthetic populations.

Build AI-powered populations that behave like your customers. Test campaigns, policies, and products before they go live—and export RLHF-ready datasets with governance built in.

Build your first populationSee how it works
3–7m
Segments in minutes
hours
Policy test cycles
RLHF + tabular
Export formats
built-in
Governance
Population Builder
Generate agents, simulate responses
Watch synthetic agents form and react to variants in real-time.
Segmenting
Embedding
Sampling

Population Engine

Population Builder

Compose audiences with geo, role, intent, psychographics, and calibration metrics. Quotas and coverage update live before you run simulations, so every run is statistically grounded.

Calibrated cohorts
Alignment scores and confidence intervals per segment.
Quota aware
Lock minimum responses per geo or persona before execution.
Population coverage
By geo, role, intent, calibration
Quotas locked
15 segments
Calibration
0.93 alignment
Coverage
98% quota met
Ready for runs
Segments validated
Synthetic Research & Polling Lab
Live experiment snapshot
Variants, tallies, and rationale sampling in one view.
Variants
  • A — "Discover faster"Control
  • B — "Automate research"Variant
Live tallies
A62%
B38%
Quota met: 98% | Segments validated
Responses
12,480
Segments
15
Export
RLHF JSONL

Synthetic Research & Polling Lab

Segmented experiments with governance built in

A/B/n tests, polling, scenario simulations, and preference judging with quota controls, audit trails, and exportable rationales. Designed for product, marketing, and risk teams to move together.

Variants & guardrails
Quota controls, overrides, and safety policies per segment.
Observability
Latency, token spend, alignment score, and win rates per run.

Workflow

How Synthetic Labs works

From segment definition to RLHF-ready exports—every motion is production-ready.

1Step 1
Define segments
Describe the population, constraints, and policy levers you want to test.
2Step 2
Calibrate behavior
Tune agents against real-world priors, guardrails, and evaluation suites.
3Step 3
Run experiments
Simulate interventions, campaigns, and product changes with repeatable runs.
4Step 4
Export & govern
Produce datasets and reports with privacy, utility scoring, and audit trails.

Use cases

Where teams deploy Synthetic Labs

Purpose-built for marketing, product, policy, and ML teams who need evidence before launch.

Pre-launch policy testing
Test pricing, onboarding flows, and safety constraints before shipping changes.
Campaign and messaging research
Run synthetic market research to de-risk positioning and channel strategy.
Rare event exploration
Stress-test edge cases and long-tail segments that don’t show up in early data.
RLHF-ready dataset generation
Generate curated prompt/response pairs with guardrails and export pipelines.

Why AuraOne

Synthetic populations vs. traditional research

Iteration speed
AuraOne: Repeatable runs in hours with full audit trails.
Traditional: Weeks of recruiting, interviewing, and aggregation.
Coverage
AuraOne: Target rare segments and edge cases on demand.
Traditional: Biased toward available participants and short surveys.
Reproducibility
AuraOne: Deterministic replays with fixed configs and seeds.
Traditional: Hard to reproduce human studies exactly.
Governance
AuraOne: Privacy budgets, utility scoring, and compliance artifacts.
Traditional: Manual documentation and inconsistent provenance.

Proof

Trusted by teams that move fast

“We validated three rollout options before field data arrived and shipped with confidence.”
Head of Growth, B2C SaaS
“The synthetic population runs let us measure drift and fairness without waiting on surveys.”
Applied Research Lead
“Exports dropped straight into our RLHF pipeline with the audit trail we needed.”
ML Platform Engineer

Launch with confidence—without waiting for field data.

Run experiments on calibrated synthetic populations, export RLHF-ready datasets, and ship with governance built in.

See a live runExplore synthetic data
Synthetic Labs