To show this in practice, we ran all 101 Super Bowl LX ads through Mavera on game night: https://superbowl.mavera.io. We simulated how audiences would emotionally and behaviorally respond by platform and segment. We returned a distribution rather than a single score as part of a full analysis of each ad in under 4 hours.
The model is a GAN adapted for language, emotion, and cognition. A generator produces synthetic audience responses and a discriminator validates them against human benchmarks. Scoring follows a feel-think-act framework: emotional activation, cognitive framing, behavioral prediction. We validated scoring against the Harvard/Illinois OASIS benchmark. MAE on emotional response is 0.02-0.15 versus 1.0-2.5+ for GPT and Claude. Every response includes a confidence score and a hallucination risk score. You can also build-in spread of opinion, response stability, and impact of news/market context scores to your outputs.
The API is OpenAI-compatible. Change the base URL to app.mavera.io/api/v1, add a persona_id, and you are running against 50+ pre-built personas or you can customize your own. Sub-100ms latency at P99. Free API key and docs at https://docs.mavera.io/introduction.