IdeaRank AI

IdeaRank AI

Instant financial modeling & validation for startup ideas

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Most AI validators just chat. IdeaRank AI does the math. We turn ideas into investor-ready financial models in 60 seconds. Get instant NPV, IRR, and Payback Period calculations plus a proprietary MOAT score to measure defensibility. Stop guessing; build with data-backed confidence.
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Launch tags:Fintech•SaaS•Artificial Intelligence
Launch Team
AssemblyAI
AssemblyAI
Build voice AI apps with a single API
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What do you think? …

Benon Nyabuto
Maker
📌
Hi Product Hunt! I’m Benon, the maker behind IdeaRank AI. What inspired me & The Problem: As a Structural Engineer by trade, I’m used to calculating loads and stresses before a single brick is laid. If the math doesn't work, the building collapses. I realized that in the startup world, we do the exact opposite. We build products first and check the "business math" later. I saw too many founders (myself included) fail not because of bad ideas, but because of broken business models. I wanted to build a "structural engineer" for startups, something that checks if the financial foundation can actually hold weight before you start building. How the process evolved: When I started building IdeaRank, it was just a generic "idea validator." But I quickly realized that generic AI advice like "This sounds promising!" is useless. My process shifted from "creative writing" to "financial engineering." I had to iterate heavily on the backend to force the AI to stop hallucinating and start calculating: From Text to JSON: I completely re-engineered the system to output strict JSON data, enforcing mathematical accuracy over creative flair. Hard-Coding Reality: I realized Silicon Valley metrics don't apply everywhere. I had to bake in specific constraints, like local tax rates (30%) and discount rates (12-18%), to make the models realistic for emerging markets like Kenya. The "Human" Variable: Finally, I realized that even a perfect idea fails if the founder isn't ready. That’s why I added the Readiness Quiz late in development, to assess the founder's stage (Dreamer vs. Builder) alongside the idea's financials. I’d love for you to try it out. Enter an idea, let the system run the NPV and MOAT calculations, and let me know if the financial reality matches your expectations! I'll be here all day answering questions. Benon
Masum Parvej

@benon_nyabuto The JSON output angle is smart. How does the system handle edge cases where data is incomplete or messy?

Benon Nyabuto
Maker

@masump Great question! The system handles it with a 3-layer safety net:

1. Strict Schema Enforcement (The Guardrails): the system has a strict JSON Structure with explicit rules. For example, e.g., competitorCount must be a single integer, not a range like '5-10'). If the input is messy, the strict schema forces the system to normalize it into a usable format before it ever hits our frontend.

2. Intelligent Gap-Filling (Contextual Imputation) Users rarely have all the data (like Churn Rate or CAC) when they just have a napkin idea. Instead of failing, the System fills these gaps using realistic market data.

  • Example: If a user doesn't provide a tax rate, the system automatically applies the corporate tax standard (~30%) rather than guessing or leaving it null.

3. Code-Level Sanitization (The Cleaner): there's a cleaning function in the backend that locates the first { and the last } in the response string. This strips out any text outside the JSON object before we attempt JSON.parse, ensuring the app doesn't crash.