How I built an AI tool to stop guessing software architecture from bad requirements

A developer built Noesor, an AI-powered platform that converts ambiguous text requirements and PDF documents into structured technical foundations before coding begins. The tool's contextual engine extracts entities such as User, Transaction, and Booking and maps their relationships, aiming to replace the guesswork developers face when estimating software architecture from vague client briefs.

Hey everyone 👋 We've all been there. A client or product manager hands you a 2-paragraph document or a chaotic PDF and says: "We need a simple Uber for X, please give us a technical estimation and the architecture by tomorrow." You spend the next few hours scratching your head, guessing the database entities, mapping potential modules, and praying you didn't miss a massive technical blind spot that will ruin the project two months from now. I got so tired of this "improvisation phase" that I decided to build a solution for it: Noesor noesor.com . Noesor is an AI-powered platform designed around the Design-First philosophy. Its core mission is simple: transform raw, ambiguous text requirements or PDF documents into a clear, structured technical foundation before you write a single line of code. Instead of jumping blindly into coding, it acts as an intelligent bridge to help you make well-founded architectural decisions. When you input a requirement, the contextual engine processes the text and automatically extracts: User , Transaction , Booking and maps how they relate to each other. As a developer, I hate signing up for tools just to see if they actually work. That's why I designed Noesor with a strict no-friction onboarding : I built this to solve my own pain points, but now I want to make it awesome for the global dev community. Go to noesor.com, throw your most chaotic, poorly written requirement at it, and tell me: Drop your thoughts, feedback, or roast in the comments below 👇