Nicholas wanted to build speech analysis technology for pronunciation training. The vision was clear: help users test their pronunciation against AI models and get scored feedback.
No-code tools could build the frontend, but couldn't connect to the complex backend: automated speech analysis engines, pronunciation simulators, real-time audio processing. He was stuck between the interface he could build and the functionality he needed.
"Not knowing code and how to build the backend. Other non-code solutions like bubble.io only got me part of the way there and ultimately felt more like a front end system."
The scoping discussion was clear, crisp, and concise. By focusing on the core outcome—linking the automated speech analysis backend to a user-facing frontend—the path forward became obvious.
The decision: build a platform where users can input speech, test it against a pronunciation simulator, and receive feedback. One complete workflow. Not every feature imagined, just the essential loop that proves the concept works.
"Having a technical co-pilot. A person able to translate outcomes into code. It's refreshing to hand over work and to have someone who will carry some of the burden. As a non technical solo founder it's an emotional assistance in some way."
A complete platform connecting automated speech analysis APIs to a user-facing interface. Users can input speech, test pronunciation against AI models, and receive scored feedback—all in a clean, mobile-optimized UI.
Backend integration with speech recognition APIs, real-time audio processing and scoring, mobile versus desktop UX optimization, data flow between frontend and analysis engine. Everything needed to test the core hypothesis.
Nicholas had been stuck for months trying to bridge the gap between idea and implementation. The MVP proved the core technical concept works.
Now he can test with real users and iterate based on feedback, not assumptions about what speech analysis should look like.
The platform is complete enough to put in front of users who need pronunciation training. He can now gather real feedback on whether the AI analysis provides value, whether the interface makes sense, and what features matter most.
He's no longer paralyzed by the gap between frontend and backend. He has a working system that connects both, ready to test with real users.
Most early products fail at the starting line—not because the idea is wrong, but because the first version tries to do too much. Zorentia gives you a precise, engineering-grounded starting point: the smallest complete unit of value your product must deliver before anything else makes sense.
We help you shape the idea, define the smallest complete unit of value, and hand back a plan for what to build first—and what to add next once it works.