A clean number off a bank statement means nothing if you cannot prove where it came from. Pull the ending balance from a six-page statement and you still have a problem. A reviewer has to trust it, and a bare figure gives them nothing to check. Agentic Document Extraction (ADE) closes that gap at the output. ADE parses the statement and extracts every figure. Each value comes tied to the exact page and box it was read from. On the sample stated below, ADE pulls 278 fields from six pages, including all 47 transactions in the register. Click any one in the output and it highlights on the page, right where it sits. For anyone in a bank or an audit seat, that link is the whole point. The number and its source travel together, so a reviewer checks a value instead of hunting for it. Run the bank sample yourself. Link in the comments.

The documents you run through Agentic Document Extraction (ADE) are rarely clean. They arrive bundled, mixed in type, and hundreds of pages long. ADE handles that with six operations you compose into a pipeline: Parse - make the document computable. Extract - request structured fields as output. Classify - determine the document type. Split - break large documents into components. Section - generate a hierarchical table of contents. Grounding - show exactly where every value came from. The order is where the leverage sits. Full breakdown in the comments.

High-volume document extraction with Snowflake insertion using DPT-3. Most batch pipelines fill the warehouse at the end. The extractor works, the warehouse waits, then everything lands at once. This one streams each document the moment it is ready, so rows appear the whole time a batch runs. Agentic Document Extraction (ADE) Gen2 does the reading. Parse v2 returns a consistent hierarchy of pages, blocks and lines, with tables as HTML so the row and column structure survives. Extract v2 works over that structure and fills the schema you define. What lands in Snowflake: → Header fields → Line items → Parsed blocks → Full document markdown Every value traces back to the exact line it came from, with a bounding box, which is what makes an audit answerable rather than approximate. The throughput comes from three choices. Mini-batching, staged files bulk-loaded rather than inserted row by row, and parallel insertions. Neither side sits idle. Invoices are the worked example. Swap the schema and the same flow handles bills of lading, statements or forms. Get the full pipeline on GitHub. Link in comments.

Most companies pay $12.88 to process one invoice. The best pay $2.78. Almost none of that gap comes from better software. It comes from what happens between the invoice landing in an inbox and someone approving the payment. The usual fix is a new AP suite, months of migration, and a bottleneck that survives all of it. Format chaos is where most builds actually break. One vendor sends a clean PDF, another a photocopied scan, a third calls the same field Invoice #, Inv No. or Factura, and the template that handled the first two returns nothing on the third. Agentic Document Extraction (ADE) handles it in two stages: → Parse normalizes the document, holds the layout → Extract pulls 20 plus fields into a Pydantic schema Same invoice, either side of that, below. Every field stays optional, which sounds like a small decision until extraction misses one and the whole pipeline takes down a night of processing instead of flagging a single document. Then matching, where most AP automation quietly stalls. An invoice matches a purchase order when the amount difference falls inside $1 absolute or 2% relative, whichever is larger. Two thresholds, because one fails both ways. Rounding kills the small invoices, shipping adjustments kill the large ones. Full write-up in the comments.

Your extraction returns the right value. It cannot tell you where that value came from. ADE Ground fixes that. Agentic Document Extraction (ADE) Ground takes the extraction_metadata from ADE Extract and the structure tree from ADE Parse. It returns every field mapped to the blocks it was quoted from, each with a block id, a page number, and a bounding box. Load parse.json, load extract.json, POST both. The join is stateless and deterministic. And it runs synchronously and consumes no credits. Four things that become possible: ➡️ Highlight values on a rendering of the page ➡️ Build review interfaces where a person clicks a field and sees its source ➡️ Attach citations to extracted data ➡️ Crop the region a value appears in A field comes back null when nothing in the document was quoted for it. The model synthesized the value or found nothing. Want to learn more? Docs link in the comments.

We asked the LandingAI team what they think about Agentic Document Extraction (ADE) Second Generation. Document grounding came up most. ADE Second Generation reads a document hierarchically, from a section to a paragraph to a line to a single value. That granularity is what makes citations precise. One bounding box per rendered line lets you trace any value back to its exact spot on the page. For regulated teams, that changes the review loop. One healthcare customer runs millions of documents a month, with 70 to 80 people verifying results by hand. Cost came up next. Customers now pick a service tier, and DPT-3 Verity brings high accuracy to simpler documents without the full agentic cost. The team also pointed to the work customers never see. Evals, checkbox accuracy, and the distance between a prototype and a production stack. Full clips from the team in the comments.