Concept
Concept
Startup validation is slow, biased, and resource-intensive. Henneth replaces manual interviews and guesswork with AI-generated user simulations that interact with pre-release builds like real customers. These synthetic agents stress-test adoption, navigation, and retention, providing structured, data-backed insights for rapid pivots.
Scope
Scope
We built Henneth with a multi-layered architecture: prompt-engineered LLMs for idea refinement, agent-based simulation for behavioral testing, Playwright-driven UI/UX evaluation, and compute-optimized pruning to scale simulations efficiently. The platform has been used by 38+ startups, simulating 30k users, reducing R&D spend by 35%, and compressing validation cycles by 8×.
Concept
Startup validation is slow, biased, and resource-intensive. Henneth replaces manual interviews and guesswork with AI-generated user simulations that interact with pre-release builds like real customers. These synthetic agents stress-test adoption, navigation, and retention, providing structured, data-backed insights for rapid pivots.
Scope
We built Henneth with a multi-layered architecture: prompt-engineered LLMs for idea refinement, agent-based simulation for behavioral testing, Playwright-driven UI/UX evaluation, and compute-optimized pruning to scale simulations efficiently. The platform has been used by 38+ startups, simulating 30k users, reducing R&D spend by 35%, and compressing validation cycles by 8×.
