ReachLLM to track, analyze and improve AI Search visibility

Hacker News

I built ReachLLM, which ranked top 5 in Product Hunt, but it still needs early adopters to improve. The goal of the app is that it's a self-serving SaaS to track, analyze & improve AI search visibility (ChatGPT, Perplexity, Grok, Gemini, DeepSeek, and Claude). The coolest features are: - Brand Visibility to see in numbers, exactly how many times your brand is showing up in all AI models, and compare with the competitors. - GEO Audit that checks through 20 different parameters to show the strengths and weaknesses in terms of all the best practices of GEO (Generative Engine Optimization). - AI generator, where you can create different content to improve the visibility, and with the Brand Monitor to track progress. The idea is to make it accessible for everyone and easy to use, so even small SMBs and SMEs don't fall behind in this AI Search Era. I would love to hear feedback or answer any questions. Here's the PH launch for more context: https://www.producthunt.com/posts/reachllm-2?utm_source=othe...

Visit product View on Hacker NewsAug 25, 2025

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

83%83% 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: para, gemini · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

26%26% 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

17%17% 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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