AI bot that automatically processes unstructured documents

Hacker News

Hi HN! We’re excited to share what we’ve been working on—a bot that automates the tedious task of processing unstructured documents from emails and entering them into ERPs. After many iterations, we’ve achieved 99.8% accuracy in extracting and mapping data from invoices, POs, and other documents. One surprising takeaway from this journey: building the AI was only 10% of the challenge! The real work came from handling edge cases, integrating seamlessly with various ERPs, and creating a reliable pipeline for real-world documents with messy formats. We’d love your feedback, thoughts, or questions about how we built this, the challenges we faced, or anything else. Let us know what you think! Thanks for checking it out!

Visit product View on Hacker NewsNov 23, 2024

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos

67%67% 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, pipe, io · Missing: https docs, just released, exist

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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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