The Document Intelligence Layer
Extracts the data buried in contracts, invoices, and PDFs automatically
How It Works
The Document Intelligence Layer receives incoming documents: contracts, invoices, intake forms, applications, or any structured PDF. It extracts the defined data fields, validates completeness and consistency, and writes the extracted data to your system of record. High-confidence extractions process automatically. Low-confidence or exception cases route to a human reviewer with the relevant section highlighted and a suggested correction.
Step-by-Step Flow
Define the document types and fields you need to extract for each
Connect your document intake channel: email, upload portal, or shared drive
AI extracts defined fields from each incoming document automatically
High-confidence extractions write to your system of record automatically
Low-confidence or exception cases route to human reviewer with context
Reviewed corrections log back to improve extraction accuracy over time
Best For
- Operations teams manually re-entering data from incoming documents
- Finance teams processing 10+ invoices per week without automated extraction
- Professional services companies reviewing contracts and intake forms at volume
This is customized for your business.
Every node, tool, and logic path shown here gets adapted to your team structure, your CRM, and your existing workflows. What you see is the proven pattern. What we build together is built specifically for you.
Implementation Notes
Document ingestion supports PDFs, scanned images via OCR, Word documents, and Excel files. For incoming email attachments, documents are captured via Gmail or Outlook API or a shared inbox parser. For upload portals, files are accepted via form with API webhook trigger. The extraction pipeline uses a structured approach: first, document type classification to identify which schema to apply; second, field extraction using a combination of positional rules (for standardized documents like invoices) and LLM-based extraction (for variable-format documents like contracts). Extracted fields are returned as structured JSON with a confidence score per field. Fields with confidence above 0.85 are accepted automatically. Fields with confidence below 0.85 or where extracted values fail validation rules are flagged for human review in a queue interface. The review interface shows the original document with the flagged section highlighted alongside the extracted value and confidence score. Human corrections log back and are used to fine-tune extraction prompts for that document type. Compatible write-back systems include QuickBooks, NetSuite, Salesforce, Airtable, Google Sheets, and any platform with an API. Median extraction accuracy on standardized invoices exceeds 97 percent after a two-week calibration period. Prerequisites: a defined schema of fields to extract per document type and a sample set of at least 20 representative documents per type.