Crovise – An LLM that uses static analysis to generate CRO hypotheses

Hacker News

Hi HN, I’m Adam. Over the last 8 months, I’ve been learning the SaaS stack and building small side projects. One thing I consistently struggled with was conversion rate optimization. Most guidance felt subjective, and A/B testing wasn’t realistic with low traffic. I built Crovise to explore whether static analysis could be used to generate useful CRO hypotheses. Instead of tracking users or predicting outcomes, it analyzes the HTML and DOM structure of landing pages: semantic tags, hierarchy depth, CTA placement, and common structural patterns. The idea is closer to static code analysis than analytics — surface potentially weak or interesting structures that are worth testing. Technically, the system is rule-based by design and built with Next.js. The hardest part was translating qualitative UX heuristics into deterministic rules without making them too rigid. This is an MVP and is currently in a waitlist phase. It works best on simple marketing pages and struggles with complex SPAs or highly dynamic content. False positives are expected. Thanks for reading.

Visit product View on Hacker NewsDec 28, 2025

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74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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