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Preferences AI Logo
Inspiration
42% of startups fail for a reason: they spend months building and burning capital to find out that nobody actually wants their product. Traditional market research is slow, expensive, and riddled with human response bias. Founders spend thousands of dollars and months of development time just to get a basic signal on whether their product has a market.
We asked ourselves: What if founders could test their ideas against a simulated, highly targeted market demographic in seconds before writing a single line of code?
We saw the perfect opportunity to build ValidationLab AI—a frictionless, Agent Service Provider that acts as a 24/7 product-market fit concierge for founders worldwide.
Capabilities
ValidationLab AI turns raw ideas into data-validated ventures through a multi-tiered agentic workflow:
Users input a raw startup idea. The system immediately processes it, delivering a high-level preview report containing a SWOT analysis, initial target segment ideas, and potential market obstacles. Users then unlock the Preferences AI Sandbox using either Stripe or a web3 wallet. The system spins up hundreds of distinct, synthetic LLM "persona" agents custom-tailored to represent the user's targeted customer demographic. It runs a deep behavioral survey simulation against this digital population to generate interactive data charts, sentiment scores, and churn projections. Finally, the system transforms the verified digital population data directly into a highly tailored, investor-ready pitch deck outline on a dedicated premium execution screen.
How we built it
We engineered ValidationLab AI to be highly modular and scalable.
Our frontend is built with Next.js and CSS, styled in a sleek dark theme, and deployed on Vercel for lightning-fast performance and global availability.
Agentic Workflows: Developed using autonomous agent loops (Hermes Agent) that process the initial concept, dynamically construct surveys and simulations of digital populations from 2 distinct target demographics using Preferences AI API, and format structural slide layouts for the pitch-deck compiler using Hermes Agent again.
Challenges we ran into
Vercel Serverless Expirations: Running large-scale digital population simulations takes time. Standard Vercel serverless functions would occasionally time out (504 Gateway Timeouts) during heavy agent loops. We solved this by implementing an asynchronous polling architecture: our Vercel frontend triggers the run, our background workers execute the simulation, and the frontend polls for updates.
Designing a web3 checkout flow that felt as intuitive as Web2 was difficult. We spent significant effort optimization-testing our wallet integration to ensure the web3 Wallet extension popped open smoothly, displayed clear transaction goals, and processed payments instantly.
Accomplishments that we're proud of
We didn't just build a standard web app; we designed a fully compliant Agent Service Provider capable of executing end-to-end agentic workflows (from market simulation to document synthesis) in under 90 seconds.
Our persona-generation prompt chain creates distinct, highly opinionated digital consumers who mock-purchase, critique, and interact with ideas realistically, providing genuinely useful feedback to developers.
What we learned
The Power of the Model Context Protocol (MCP): Designing our backend services to act as discrete, programmatically callable agent tools opened our eyes to the future of Agent-to-Agent (A2A) commerce.
What's next for ValidationLab AI
A2A (Agent-to-Agent) Marketplace Integration: Exposing our crowd-simulation tool natively as an MCP tool on the marketplace, allowing other autonomous venture agents to hire ValidationLab to audit their portfolios programmatically.
Infusing our digital populations with real-time web search capabilities, letting simulated personas make buying decisions based on current real-world pricing and competitor landscapes.
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