The issue wasn't the building process, it was accuracy. Most AI applications plateau at 60%~ reliability, which is fine for prototypes but it's unusable in production. We realized these tools aren't really "AI app builders", they're website builders that happen to use AI.

We wanted to solve the hardest problem first: making AI applications actually work reliably.

Our approach centers on what we call dynamic optimization. Instead of cramming every possible scenario into one massive prompt (which confuses LLMs), our system adapts contextually. A travel chatbot automatically knows to mention LAX for Los Angeles vs. Pearson for Toronto. This consistently delivers 90%~ accuracy versus the industry standard 60%~.

But accuracy alone wasn't enough because we also needed to solve the builder gap:

- Simple builders (Lovable, Bolt): Create static websites, not AI applications

- Complex ML tools: Require dedicated teams most startups don't have (Arize, Voxel51) - we've also heard from both technical and non-technical founders that they found these tools very complex

- What's missing: Tools that build applications where AI is embedded functionality

So we built AI agents with optimization built-in. Users just type what they want to build and our agents handle the full development pipeline: creating applications with embedded models, RAG and intelligent processing. You can deploy to your own infrastructure via Netlify, GitHub or download it directly since you can run it locally.

The result: Startups, solo hackers and enterprises can build production-ready AI apps without hiring a dedicated ML team.

Waitlist: https://empromptu.ai

We'd love feedback from the HN community — esp. if you've hit similar accuracy problems or thoughts on the technical approach.