Document Extractor - Turn PDFs into structured data
One template. Every PDF. Clean data.
Manual data entry from PDFs is slow, error-prone, and impossible to scale. Document Extractor lets a non-technical user teach the system once - drawing fields on a sample PDF - and from then on, every uploaded document of that type comes back as structured JSON, CSV, or directly into your downstream tools.
Drowning in PDFs, one re-key at a time.
An operations team was processing thousands of vendor invoices, lab reports, and shipping documents every month - each in a slightly different layout. Their workaround was a rotation of contractors copy-pasting fields into spreadsheets. Errors compounded downstream, vendor onboarding took weeks of regex tweaking, and the team's most expensive hires were stuck doing data entry. They asked us for an automation that any team member could maintain themselves.
Every vendor a snowflake. Every script a liability.
Off-the-shelf OCR tools either hallucinated fields or required engineering hours to configure for each new document type. Generic LLM extraction worked on a clean sample but failed silently on edge cases - misaligned columns, multi-page tables, scanned pages with shadows. The team needed extraction that was deterministic on the fields they cared about, transparent when something looked wrong, and editable by the people closest to the documents.
Teach it once. Let it run forever.
We built Document Extractor as a template-driven extraction platform. A user uploads a sample PDF, draws boxes around the fields they want (invoice number, line items, totals, vendor address), names them, and saves the template. Every future PDF of that type gets matched to the template automatically - text, tables, and signatures extracted into structured output, with confidence scores and a side-by-side review view for anything ambiguous.
Visual template builder
Draw extraction zones directly on a sample PDF. Field names, types, and validation rules live in the template - no code, no regex, no ML expertise required.
Auto template matching
New uploads are classified against existing templates by layout fingerprint. Ambiguous matches get queued for human review; confident ones run end-to-end automatically.
Tables, scans, and multi-page
Handles scanned PDFs via OCR, line-item tables that span pages, and rotated or noisy documents. Outputs include cell-level coordinates for downstream auditing.
Direct integrations
Push extracted data straight into Google Sheets, your accounting system, or any webhook. Per-template field mapping means downstream tools never see raw extraction noise.
Hours of data entry, gone.
The team replaced their copy-paste workflow with templated extraction across more than 30 document types. Onboarding a new vendor format dropped from a multi-day engineering ticket to a 15-minute template draw by an ops analyst. Errors became traceable - every extracted field links back to the exact pixel region it came from - and the engineering team got their backlog back.
- ✓ MVP delivered
- ✓ Full product built
- Live — standalone SaaS
See solution in action
Open solution →RAG systems & knowledge bases
Once you have structured data, plug it into a searchable knowledge layer for plain-language queries.
Multi-agent workflow automation
Extend extraction with downstream agents that route, validate, or escalate documents based on content.
AI strategy & audit
Mapping which document workflows to automate first - and which to leave alone.
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We build custom AI systems for small and mid-size businesses - from working prototype to production, with a clear process and defined outcomes at every step. No generic tools, no long commitments before you've seen results.
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