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SuppFlow – AI assistant that replies to your customer support emails

Hacker News

SuppFlow – AI assistant that replies to your customer support emails

Hi HN I built SuppFlow.com – an AI assistant that helps you manage and respond to customer support emails automatically. It reads incoming emails, drafts helpful replies, and saves hours of manual work for solo founders and small teams. The goal is to reduce response time and keep support quality high – without hiring more agents. It's currently just a landing page while I validate demand. Would love any feedback – especially from folks handling support daily!

Visit product View on Hacker NewsJul 20, 2025

Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, email · Missing: mac, macos, cursor

81%81% 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

78%78% 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

41%41% 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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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