I built a tool to track when AI engines cite your website

Hacker News

Hey HN! I built a tool that tracks when AI search engines (Perplexity, ChatGPT, Claude, Google AI) cite your website in their responses. *The problem:* AI search is exploding (Perplexity does 100M+ queries/month), but there's no way to know if these engines are citing your content. It's like SEO in 2005 before Google Analytics existed. *What it does:* - Automated daily checks across multiple AI platforms - Tracks citation rate, position, and trends over time - AI-powered recommendations for queries where you're NOT cited - Shows which platforms cite you most *Technical approach:* - Puppeteer for web scraping (no expensive APIs) - Firebase for real-time data - Groq for AI recommendations - Next.js + TypeScript frontend - Vercel cron for automation *Why I built it:* I run llmscentral.com (a repository of llms.txt files) and noticed traffic from Perplexity was growing 50% month-over-month. But I had no idea which queries were driving it. Built this to scratch my own itch. *Free tier:* 3 tracked queries, weekly checks on Perplexity *Paid tiers:* More queries, daily checks, all platforms, AI recommendations Try it: https://llmscentral.com/citation-tracking Would love feedback from the HN community! Happy to answer any technical questions.

Visit product View on Hacker NewsOct 26, 2025

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

87%87% 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 · Missing: supports, reddit linkedin, podcasting

76%76% 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, way · Missing: mobile apps, ios, personal

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

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

36%36% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

31%31% 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

18%18% 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, paid · 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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