Mavera – Predict audience response with GANs, not LLM sentiment
Mavera is an audience intelligence API. Give it a message, product prototype, or creative asset and it returns a predicted distribution of emotional and behavioral responses across your target stakeholder population. This is the best way to test your assumptions before you spend or push anything live. 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.
✨ Resumen de IA
Mavera is an audience intelligence API. Give it a message, product prototype, or creative asset and it returns a predicted distribution of emotional and behavioral responses across your target stakeholder population. This is the best way to test your assumptions before you spend or push anything live. 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.
Ideal para
Desarrolladores, equipos de producto y fundadores técnicos.
Por qué importa
Mavera is an audience intelligence API. Give it a message, product prototype, or creative asset and it returns a predicted distribution of emotional and behavioral responses across your target stakeholder population. This is the best way to test your assumptions before you spend or push anything live. 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.
Funciones clave
- Mavera is an audience intelligence API.
- Give it a message, product prototype, or creative asset and it returns a predicted distribution of emotional and behavioral responses across your target stakeholder population.
- This is the best way to test your assumptions before you spend or push anything live.
- To show this in practice, we ran all 101 Super Bowl LX ads through Mavera on game night: https://superbowl.mavera.io.
Casos de uso
- Review original launch sources before making adoption decisions.
- Track community momentum from Product Hunt, GitHub, and Hacker News.