I started this after building a few small SaaS projects where improving conversion rates often felt vague and subjective. Most advice was generic (“make the CTA clearer”) or required long A/B tests that weren’t realistic with low traffic. I wanted something more deterministic and engineering-driven.
Crovise analyzes the HTML structure and DOM hierarchy of a landing page to surface potential conversion issues and testable hypotheses. It looks at semantic structure, element placement, hierarchy depth, and common patterns seen in high-converting pages — not just visuals or copy tone.
Technical notes:
- The analysis engine is currently rule-based, not ML-heavy - Built with Next.js - Translating qualitative UX heuristics into deterministic rules was the hardest challenge
This is an MVP in a *waitlist / early-access phase*. It works best on simple marketing pages; complex SPAs or dynamic content are a known limitation, and false positives are expected.
I’m 16 and have been learning the SaaS stack for 8 months. I’d really appreciate feedback on:
- Whether static analysis is a reasonable approach for CRO - Which signals feel useful vs misleading
Waitlist/demo: https://crovise.netlify.app
Thanks for taking a look.