Agentic Document Extraction
Make the world's documents computable
116 followers
Make the world's documents computable
116 followers
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.
Agentic Document Extraction
👋 Hello! We're the team behind Agentic Document Extraction (ADE), an intelligent document processing platform, offered by LandingAI — founded by Andrew Ng.
ADE is a different way to turn any document into structured, source-cited data. It converts any document into accurate, structured data. It is fully auditable, traceable, and production-ready from day one. Trusted autonomous document processing via multiple APIs designed for real workflows. Current APIs are Parse, Extract, Classify, Section, Build Schema, Split.
🤖 Agentic reasoning — handles documents it's never seen, no per-doc fine-tuning or rule engineering
✅ Source-cited JSON — every field traces back to its exact spot on the page, down to the bounding box
💯 Confidence scores on every field — so you can trace, audit, and defend results
👨💻Composable & headless — call it from your own agents, pipe clean JSON straight into your stack
If you've been fighting per-doc-type fine-tuning or a brittle rules engine just to get accuracy you can defend, this is for you.
Throw your hardest documents at it and tell us where it holds up.
Try it at landing.ai — we're here to support! 🙌
We depend very much on Landing.ai's ADE parse feature and have not been disappointed with it. We also are paid heavy users. With this feature, we can offer our customers technical traceability with source as well as visual grounding. The independent parsing/VLM in conjunction with a general-purpose LM (e.g., Claude, GPT) has significantly increase technical accuracy of our product.
We once had issues with service/parsing latency, especially for longer documents, but their engineering team was responsive and issued a hotfix overnight. I do think the capabilities of this product have not yet been fully tapped into by other startup/companies, so do give it a try!
Agentic Document Extraction
@pkaymishr Thank you — means a lot coming from a builder using it in production. We're here to support you and help you succeed!
The per-field confidence score is the part I'd lean on hardest. In resume parsing the real failure mode is a confident extraction from a weird two-column or design-heavy layout, not a clean miss. Does the score actually drop on those out-of-distribution docs, or can it stay high while the value is wrong?