AI-Powered Document Parsing for SMEs

Hacker News

I made a tool called Parsie because I was tired of manually copying and pasting data from PDFs, invoices, and emails into spreadsheets. Running a small business means dealing with tons of vendor documents, and I just wanted an easy way to extract the important details without setting up complicated rules or templates. Parsie is an AI-powered document parser that pulls structured data from files. No coding, no complex setup—just upload a document (and optionally describe what you need). It will integrate with Zapier, so you can automate workflows with thousands of other apps. Google Workspace integration is also coming soon! I built this for small businesses and teams that don’t have time to deal with messy documents. Would love to hear what you think—feedback, feature requests, or just a “this sucks” is all welcome! Try it out at parsie.pro (there’s a free trial). Happy to answer any questions!

Visit product View on Hacker NewsMar 26, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% 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: google, apps, email · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, google, way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, 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

26%26% 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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