RankRaven – SERP tracking for AI chat models

Hacker News

Millions of people are now using chat-based LLMs like ChatGPT, Bing, and Bard to do research, however companies don't have any insight into how their products appear in the results. For traditional search engines, there are lots of SERP tracking tools that help companies analyze changes in how their domain appears in the results for different keywords, but AI models are a black box so far. I've developed a tool similar to SERP tracking apps but for AI chat models, which tracks if/where a keyword appears in model answers for specific prompts and visualizes changes over time. Would love to get your feedback and ideas for additional features.

Visit product View on Hacker NewsJul 18, 2023

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2points

Did not reach leaderboard

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

91%91% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · 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: apps, answers, visualize · 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

31%31% 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 · Missing: arr, mrr, revenue

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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