Surchee – check how AI search engines view and search your site

Hacker News

This started from a chat with my brother-in-law. He was starting a new business and planning to spend a lot of time on SEO. As i’d just started using ChatGPT more often myself, I asked him whether he was optimising for LLM answers, not just Google? Whilst Semrush and Ahrefs were leaning into this space, there was no dedicated tool looking at this. Anyway, after a few months of building I have now created Surchee: - Crawl your site and see how AI engines (ChatGPT, Perplexity, Claude, Bing Copilot, etc.) summarise it - Check if your brand, value proposition, or trust signals are coming through clearly - Track whether AI bots are even visiting your site (via a small pixel) - Use dashboards that look a bit like SEO tools, but aimed at LLM/AI visibility instead It’s quite fresh but the idea is simple: type in a URL, get a score and some insights. If anyone has any general feedback or more specific feedback per the items below, it would be appreciated: - What metrics would make this more useful - Whether “AI visibility” feels like a problem worth solving - Any rough edges or confusing parts in the current version Link: https://www.surchee.com Happy to answer any questions too.

Visit product View on Hacker NewsSep 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, google, new · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

62%62% 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, google, answers · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, 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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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