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AI That Reads Docs and Drafts Customer Support Emails

Hacker News

AI That Reads Docs and Drafts Customer Support Emails

I built an AI tool to help with customer support emails. Instead of searching through documents or relying on canned templates, it reads your uploaded docs and drafts contextual replies. The idea is to speed things up while keeping responses accurate. AI suggests replies, but you stay in control before sending. I’d love to hear your thoughts: Should AI handle more of this autonomously, or always require human review? Have you seen AI email assistants that actually work well? How do you scale customer support without losing quality? Curious to hear what the HN community thinks!

Visit productView on Hacker NewsFeb 26, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, context · Missing: mac, agents, macos
76%76% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
41%41% 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: ide, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
30%30% 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
25%25% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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