Intelligent Document Processing (IDP) Engine

Extracting structured JSON schema from complex multi-page PDF invoices, tax filings, and legal contracts.

Intelligence Gap / The Problem

Standard OCR extracts raw text but discards layout: it can’t tell you which number belongs to which line item, or which cell in a table maps to which column header. Enterprise documents like invoices and contracts are only useful once that structure is preserved.

Solution Architecture / Internal Flow

We combine layout-aware document understanding with a fine-tuned vision-language model. The layout analyzer first detects tables, key-value regions, and signature blocks on the page; the extraction model then reads text within each detected region rather than the page as a whole, which preserves the relationship between a label and its value. Output is validated against a Zod schema before it’s returned, so malformed extractions fail loudly instead of silently shipping bad data.

PDF/scan ingestion → layout analyzer (tables, key-value fields, signature blocks)
  → per-region vision-language extraction
  → schema validation (Zod contract)
  → structured JSON export

Performance Matrix / Evaluation

Metric	Falconic Protocol	Baseline
Key Field Extraction Precision	98.8%	82.0%
Table Structure Accuracy	96.5%	74.1%

Entropy Audit / Failure Analysis

Multi-page documents with tables that span a page break initially confused the layout analyzer, which treated each page independently and split single tables into two disconnected extractions. We added a page-boundary stitching pass that checks whether a table at the bottom of one page shares column headers with a table at the top of the next, and merges them when it does.

Communication Layer / Discussion

Peer Protocol Interface / Discus Integration Pending