Fiblet- We replaced chaotic inbox into smart tabs

Hacker News

I wanted to introduce Fiblet—a specialized platform built to elevate your customer support and internal communication through intelligent email workflows. With Fiblet , Groww can get benefit from: 1. Automatically classify emails by topic, which appears as a tab on the top of the dashboard. 2.Topic-based email filtering: Easily view emails related to specific subjects by clicking on topic tabs 3. Prioritize urgent or time sensitive emails. 4. Each team can handle their relevant topics. 5. Get topic wise email volume analytics to identify consumer behaviour and trending issues 6. Auto- reply to common queries using predefined FAQ templates and skip repetitive conversations 7. Helpful for audits, compliance and historical search 8. Smarter planning with data- use topic trends to improve products,services and customer experience. 9. Maintain a clean and organized inbox . If you're open to exploring how Fiblet can fit into your current workflow, I’d be glad to share a quick demo. Best regards, Sachin Sharma Founder, Fiblet Contact me via email - ss2256387@gmail.com

Visit product View on Hacker NewsJul 31, 2025

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

87%87% 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.

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

50%50% 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: platform · Missing: plus, intuitive, reviews

45%45% 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

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

26%26% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce, smart · 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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