I built a chatbot to converse with 3M SaaS product reviews

Hacker News

Hey HN, I'm Andrei, and together with my co-founder Roman have been working on a tool called Reviewradar. Working with startups, doing interviews and sifting through endless software reviews are daunting tasks. So, we built a chatbot that lets you chat with over 3 million reviews from more than 100K SaaS products. With Reviewradar, you can ask questions like: - create a comprehensive SWOT analysis for both Notion and Obsidian - give me negative feedback and complaints you have about Postmark - summarise the reviews you have on products in the OCR category I would love to get your feedback on it. Check it out here: https://reviewradar.ai Looking forward to your thoughts / suggestions, Andrei and Roman

Visit product View on Hacker NewsSep 22, 2024

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

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, plain · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% 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: reviews · Missing: plus, platform, intuitive

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

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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