Document Extraction
Reads uploaded documents and pulls structured data fields into your system of record in seconds.
// the problem
Document data sits locked in PDFs and scans, and extracting it is the kind of work that is too repetitive to be interesting and too consequential to get wrong. A paralegal pulling key dates and parties from a contract, a property manager extracting lease terms from an agreement, an accountant reading supplier details from an onboarding form. Each task is the same process: open, read, identify fields, key them in. Document extraction reads the document, identifies the relevant fields for the document type you've configured, and writes the structured output to your database or system of record. Ambiguous fields go to a human with the surrounding context highlighted, not just flagged as uncertain.
what changes
- Structured data is in your system within seconds of document upload
- Low-confidence fields are flagged with context, not silently accepted
- Source document is archived alongside the extraction for audit purposes
- Extraction schema is configurable per document type without a rebuild
// how it works
The mechanism, end to end. Each step is logged so you can see what the agent did and why.
// surface area
connects to
- Gmail
- Google Drive
- Clio
- Supabase
- Notion
- Airtable
writes back to
- Supabase record (all extracted fields, confidence scores, document reference)
- Google Drive (source document archived with extraction metadata)
all writes are logged to the audit trail
// works for
Document Extraction is built to run inside any of these business types. The same agent, wired into your stack.
// ready to scope the build?
See Document Extraction run on your workflow.
Book a 15-minute audit call. We map your real workflow against what this agent handles, scope what gets built and what it connects to, and you leave with the math. No pitch, no obligation past the call.
15 minutes, no deck, just the working machine.