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Paper Tray – dramatically better file organization for Google Drive

Hacker News

Paper Tray – dramatically better file organization for Google Drive

Hi HN, I'm a solo founder working on a project that uses AI to help with finding files in Google Drive. The Problem: With Google Docs and Sheets, it's easy to make documents but very hard to find them again unless you organize them manually, which takes time. This is a big problem for startups, who often use Google Docs for the convenience but struggle with information management. It leads to a lot of time wasted searching for documents and a lack of clarity. The Solution: Paper Tray uses AI to organize Drive files automatically. It 'tags' each file so you can then use a filter interface to find them. By default, it tags by the type of document (meeting notes, plan, pitch deck etc), the topic of the document, and the department it belongs to (product, engineering, sales etc). The result is that it takes just a few seconds to find most of your documents, in an intuitive and satisfying way. You can add your files easily with a Chrome Extension that adds a button to the header of Google Docs and Sheets. Click this button to add the file and manage the tags. Business model: a 7-day free trial followed by monthly/annual subscription, currently priced at $12/mo, or $9/mo with an annual plan.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, notes · Missing: mac, agents, macos
80%80% 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.
TrustMRRFits verified-revenue profile · Strong signals: month, google, monthly · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
38%38% 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
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 · Strong signals: subscription · 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
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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