Agentic Document Extraction

Product Hunt

Make the world's documents computable

LandingAI is on a mission to make the world's documents computable. Agentic Document Extraction helps enterprise developers build document automation pipelines at scale. We offer multiple composable agentic APIs - Parse, Extract, Section, Split, Build Schema. Each API performs a specific document intelligence task and returns accurate, auditable Markdown and JSON. Very affordable. Visual playground for prototyping at http://ade.landing.ai Accurate, auditable, API-driven document AI.

Visit product View on Product HuntJun 22, 2026API SaaS Developer Tools

Actual performance

111upvotes

4comments

Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

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

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released

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

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

25%25% 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

22%22% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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