You ship marketing every week. Nothing gets checked.

Three ways. First, every check runs across Claude, GPT, Gemini, and Llama in ensemble — no single model can be the source of all 500 reactions. Second, every cluster's accuracy is published, dated, and sourced against named ground-truth datasets — see /validation. Third, we publish predict-then-publish loops on real SaaS pricing pages: we predict the outcome before the page changes, then publish the comparison when real conversion data lands. Aaru did this for the New York Democratic primary; we do it for SaaS. Both numbers — predicted and observed — are public for every loop.

Because we publish them in a way you can audit. Every cluster on /validation lists the predicted value, the observed value, the delta, the sample size, the ground-truth source, and the audit date. If a cluster drifts below 80% accuracy, we pause it automatically. Raw audit CSVs are available on request to any customer, journalist, or academic. Most synthetic research products won't share their raw data. We do, because the only way to build trust in this category is through transparency.

No. Every check runs through nine independent corrections: multi-model ensemble across multiple independent frontier model families, calibration against named ground-truth datasets, revealed-preference weighting, distribution-shape matching, and behavioural consistency. One model wrapped in a persona prompt is one model's opinion. We give you the calibrated distribution of 500.

87% median accuracy across calibrated SaaS clusters, audited monthly. Every cluster is dated, sourced, and visible on the validation page. If a cluster drifts below 80% accuracy, we pause it automatically and notify customers.

User interviews are deeper, slower, more expensive, five hour-long conversations cost you a week and €1.5k. A check is faster, broader, cheaper, and lower-fidelity per respondent. The right pattern is to use both: Prism catches the obvious misreads in 60 seconds (don't ship the pricing-page tier in the wrong order); interviews tell you the things you didn't know to ask.

Pre-built clusters for B2B SaaS buyers (SMB, mid-market, enterprise), indie hackers and bootstrappers, dev-tool buyers, marketing-led SaaS, sales/RevOps leads, PLG users, agency owners, and API-first buyers. New clusters land monthly. See the audiences page for status, accuracy, and last-audit date.

Yes, on the Founder tier and above. Describe the audience in plain English ("engineering leads at 200–500-person infra companies") and Prism builds a cluster on the fly using the same fragment-retrieval and calibration pipeline as the named clusters.

The calibrated SaaS clusters are B2B-focused today. The same engine powers our enterprise customers' B2C work, see the enterprise page. If you're a SaaS founder targeting consumers, the indie-hacker and PLG clusters are the closest fit while we calibrate B2C-specific SaaS audiences.

No. Customer check inputs and outputs are never used as training data, full stop. We rely entirely on consent-cleared public datasets and our opt-in calibration panel. See /trust for the full data-handling policy.

Yes. Founder and Team tiers are month-to-month, cancel-anytime, prorated refund on the unused portion of the period.

Yes. Three free checks per month, no card, no signup required for the homepage demo (three streaming reactions). Sign up for an account to use the full report and access all five check types.

Live on /validation: every cluster's score, ground-truth source, sample size, and last-audit date. Clusters whose audit falls below 80% are paused automatically. Raw audit CSVs are available on request to any customer, journalist, or academic.