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Keepy AI – Automate Receipt Data Entry with an Email

Hacker News

Keepy AI – Automate Receipt Data Entry with an Email

I moved to the Bay Area to dive headfirst into the GenAI wave, eager to learn how startups build AI. I co-founded a startup with a friend and quickly realized a familiar pain: managing receipts. I'd "keep" them in my pocket, on a table, in the car – and told myself I would manage it later. Later never came. Talking to friends, I realized this was a universal struggle. That's how Keepy was born. Keepy AI automates data entry and receipt storage for individuals, freelancers and small businesses. Simply send an email with a receipt to keep@keepy.us, and our AI-powered platform handles the rest. Here's how it works: - Effortless Data Entry: No more manual inputting! Just send receipts to keep@keepy.us - Reliable Automation: 24/7 AI processing for accuracy and efficiency. - Human-Assisted Accuracy: Human agents step in if needed to ensure precise results. - Seamless Data Output: Access your data in Google Sheets, ready for analysis and integration. - Secure Cloud Storage: Peace of mind with secure cloud storage for your receipts. Try it out: send a receipt to keep@keepy.us and see how it works! I'd love to hear your feedback on Keepy. Let me know what you think!

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Actual performance

2points
3comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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: agents, agent, google · Missing: mac, macos, cursor
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
43%43% 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
41%41% 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
14%14% 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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