Invofox – the API to turn ANY document into structured, verified data

Hacker News

Hey HN! We’re Alberto, Carmelo, and Adrian, the founders of Invofox ( https://invofox.com/ ) — the document processing AI built for software companies and developers. We started two years ago with invoice processing, but we’ve since expanded our capabilities to include utility bills, purchase orders, bank statements, pay slips, receipts, and checks. Why launch on Hacker News now, especially since we’re S22 batch? As we’ve grown, one of the most common questions we hear is: “Can Invofox handle this document type too?” To solve this, we’ve just launched Custom Documents ( https://bit.ly/customdocs ) — a feature that lets you turn any document into structured, verified data. Here’s what makes it special: - No field mapping headaches: Instead of just giving you raw key-value pairs, Invofox delivers a consistent schema for each custom document type. This means you don’t need to remap fields to fit your data model—saving time and effort. - One-stop shop: With Invofox, you will be able not only to parse your documents, but also to separate and classify them, verify the data they contain, and outsource any HITL task you need to perform on the results. - Security first: Invofox is ISO 27001, SOC 2 Type 2, and HIPAA compliant, so your data is always protected. As a thank-you to the HN community, we’re offering *1,000 free documents/month for one year* (that’s 12,000 documents in total). Sign up here: https://www.invofox.com/en/hackernews-form to claim your free credits! For developers, our Developer Hub( https://developers.invofox.com/ ) has everything you need—guides, documentation, and support—to get started with Invofox or customize integrations. So far, we’ve served around 150 clients and processed tens of millions of documents, giving us unmatched experience in data quality, operational efficiency, and security. If you have questions, feedback, or ideas, I’d love to hear them—feel free to ask us anything! Best, Alberto, Carmelo, and Adrian!

Visit product View on Hacker NewsDec 12, 2024

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

98%98% 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.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

17%17% 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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