Every synthetic-data provider claims to outperform the rest, each against a benchmark it graded itself. Before you commit budget, roadmap, or credibility to synthetic research, you need an independent answer — for your questions, your categories, and your data.
Every item in your questionnaires classified along the experiential–epistemic boundary: where synthetic respondents are reliable, where they are risky, and where they are structurally unable to answer.
Response-pattern diagnostics on your existing human data — quantifying how much of your current "ground truth" is signal versus survey artifact.
Your synthetic provider or pilot evaluated on distributional fidelity, variance preservation, response diversity, and exposure to model "hard priors".
A when-to-use-synthetics playbook for your portfolio: which studies to run synthetic, hybrid, or human-only — and how to calibrate on your own first-party data.
The audit applies the Behavioral Validation Engine research program: synthetic populations validated against two years of real transaction behavior — not merely survey agreement — across 11,000+ synthetic interviews, six commercial AI models, and three countries. The frameworks used in this audit are original contributions of this program, published openly in A Buyer's Guide to Synthetic Population Solutions.
Engagements are scoped individually. Write directly to Adriana Rocha — founder, Wisdom Beyond Technology, and lead author of the BVE validation studies.
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