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Use Case
Every document that arrives as a PDF and gets manually typed into a system is a candidate for automation. We build custom extraction pipelines that read your documents, pull the fields you care about, validate them against your rules, and push the data directly into your downstream system.
The human review queue handles the edge cases, documents the AI isn’t sure about route to a reviewer rather than failing silently. Your team reviews fewer than 10% of documents on average; the rest process without anyone touching them.
Tell us about your document workflow.
Six components that together replace manual document data entry — including a human review layer for the cases the AI gets wrong.
Handles scanned documents, born-digital PDFs, and image uploads. Layout-aware extraction understands tables, multi-column forms, and checkboxes, not just plain text.
Every document produces a typed JSON or CSV output that maps to your downstream system. Fields are defined by your schema, not a generic AI guess.
Each extracted field gets a confidence score. High-confidence fields auto-approve. Low-confidence fields queue for human review instead of silently passing bad data downstream.
Business rules run after extraction: required fields, format checks, cross-field validation (e.g., totals that must match line items), and domain-specific logic your team defines.
A simple interface where reviewers see the original document side-by-side with extracted values, accept or correct fields, and release the record downstream.
Extracted, validated records push directly into QuickBooks, Salesforce, your ERP, or a database via API. No copy-paste, no re-entry.
From document arrival to downstream system, the full sequence.
Document arrives
Via email attachment, web upload portal, or watched folder. The system ingests the file and queues it for processing.
OCR and layout extraction
The document is parsed for text and spatial structure. Tables, form fields, headers, and footers are identified and separated.
LLM extraction to structured schema
A fine-tuned extraction prompt maps the document's content to your defined output schema. Each field is extracted with a confidence score.
Confidence scoring and routing
High-confidence records (above your threshold) auto-approve. Low-confidence records, or those that fail validation rules, route to the human review queue.
Human review for edge cases
Reviewers see the original document and the extracted fields side by side. They correct errors and release the record. Each correction trains the system.
Push to downstream system
Approved records push to your target system via API. The original document is archived with a full audit trail of every field and who approved it.
The right fit is a team that receives documents as a regular part of their workflow and currently handles extraction manually.
Processing 100+ vendor invoices per month across multiple formats. Currently re-keying line items, PO numbers, and amounts into an ERP by hand.
Handling claims forms, policy applications, and medical records that arrive in varying formats and require structured intake before underwriter review.
Ingesting W-9s, tax returns, pay stubs, and bank statements that need specific fields extracted and cross-validated before moving to underwriting.
If your process starts with a human reading a document and typing values into a system, this replaces that step, with a review queue for the cases the AI isn't sure about.
We’d rather tell you upfront than take a project that won’t deliver the result you need.
Fewer than 50 documents per month
At low volume, the cost and maintenance overhead of a custom extraction system doesn't justify the build. Manual processing or an off-the-shelf tool is the right answer.
Completely unstructured handwritten freeform text
If your documents have no repeatable structure (no form fields, no consistent layout, no predictable data positions) extraction accuracy will be too low to be useful. There needs to be some pattern to extract.
Tell us what documents you receive, how many per month, and where the data needs to go. We’ll reply within one business day with a rough scope and price range.