Parseflow – Automate data extraction from documents

Hacker News

Hi HN, I've been in software engineering for over 10 years and I'm excited to share my latest project, Parseflow ( https://parseflow.io ). It's a AI data automation platform designed to simplify data extraction from documents and integrate it effortlessly with your go-to applications. Parseflow can handle a variety of document types, including PDFs, images, and scanned documents. This means you can easily extract data from invoices, receipts, contracts, and more. Once you've extracted the data, you can integrate it with a variety of applications, including Google Sheets, QuickBooks, Slack and 3000+ more through Zapier. This makes it easy to automate tasks and workflows that would otherwise be manual. Parseflow is free to use with a limited number of credits, so you can try it out for yourself and see how it can help you automate your data extraction tasks. Feel free to reach out to me at dev@parseflow.io if you have any questions regarding your specific use case. We're happy to help you explore how Parseflow can help you! Looking for feedback on: • What do you think of the idea? • Do you do data extraction? If so, do you do it manually or via a similar service? • What is your use case?

Visit product View on Hacker NewsOct 28, 2024

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

82%82% 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: slack, google, tasks · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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.

Incorrect prediction on native model

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