Citable

Product Hunt

Be in AI answers before your competitors. We do it for you.

Citable helps brands show up in AI answers. We simulate real personas by pre-warming AI accounts (ChatGPT, Perplexity, Gemini, Claude, Grok) with memory, then run thousands of prompts daily to measure share-of-voice and capture every cited source. We surface gaps by persona, market, and model—and explain why. Finally, we give prescriptive actions: publish specific articles, join targeted Reddit threads, and prioritize the highest-leverage plays for your team.

Visit product View on Product HuntMar 11, 2026Artificial Intelligence Search Marketing automation

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Actual performance

148upvotes

17comments

Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

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

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

30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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