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Kupiks – AI powered email parsing

Hacker News

Kupiks – AI powered email parsing

Hey, HackerNews community! I’m Muhammed, the founder of Kupiks ( https://kupiks.com ) an email parsing solution specifically built to efficiently analyze and extract data from emails, integrate it with CRMs, and offer the flexibility to export data as needed, it allows customer support, sales, and e-commerce teams to optimize their workflow and eliminate manual data entry, saving time and effort. I created Kupiks to address a significant challenge I faced as a busy freelancer – the overwhelming task of sorting through a large volume of emails and manually extracting information to enter into CRMs and spreadsheets. Kupiks solves this issue by giving you the ability to define the data you want to extract, create a custom mailbox you can forward your emails to, and automatically input data to an integration of your choosing. Our solution is being used by users to capture leads, parse and categorize customer support inquiries, as well as Invoice, and Receipt Parsing. Kupiks beta is now open to the public, sign up here https://kupiks.com to receive an invite.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, efficiently · Missing: supports, reddit linkedin, podcasting
88%88% 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: user, new, email · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, 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
36%36% 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
12%12% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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