AI vision and OCR that turns paper into structured data.
Receipts, invoices, IDs, tax forms, contracts, photos. Extracted to structured data, validated against your schema, routed into the system you already use. Built for tax firms, clinics, contractors, and operations teams.
What vision and OCR replaces.
Most small businesses still type a lot of data off paper. Tax firms type W2s and 1099s into prep software. Clinics type insurance cards into the EHR. Contractors type receipts into accounting. Insurance teams type quote intake forms. The work has rules; AI just needs to read the document.
Vision plus OCR plus an LLM works better than legacy OCR alone. Legacy OCR struggles with hand writing, low resolution photos, and unusual layouts. Modern vision capable LLMs (Claude 4, GPT 4o) read receipts and forms with accuracy that approaches a human, and they extract to a schema you define.
The architecture under the hood.
Document arrives via upload, email, or mobile capture. The document is sent to a vision capable LLM along with a structured output schema describing the fields to extract. The LLM returns JSON matching the schema. Validation rules run on the JSON: required fields, date formats, dollar amounts, totals match line items. If validation fails, the document goes to a human review queue. If it passes, fields are written to the destination system.
For high volume document types we maintain a confidence score per field and a per document overall confidence. Documents below a threshold go to human review automatically. Above the threshold, they go straight through.
Sources: Anthropic Vision, OpenAI Vision, AWS Textract for legacy OCR comparison.
What we build with.
Default vision and OCR stack.
Vision LLM
Document ingest
Schema validation
Destination
Industries that benefit most.
What vision and OCR costs.
Per document cost depends on size and complexity, typically $0.01 to $0.05 per document. Volume discounts kick in above 10,000 documents per month. We instrument cost per document so the unit economics are visible.
Vision and OCR FAQ.
How accurate is it?
For typed documents (W2, invoice) above 99 percent. For hand written or poorly lit photos closer to 90 percent. We tune the human review threshold so accuracy at the destination matches your tolerance.
What about PII?
For PII workloads we deploy on private inference or BAA compliant infrastructure. Documents are not sent to public LLM APIs without consent and DPAs.
Can it handle multi page documents?
Yes. The pipeline handles 1 page receipts up to 100 page contracts. Larger documents are processed page by page with consolidation at the end.
What if the format changes?
Vision plus LLM is more robust to format change than template based OCR. If the IRS changes the W2 layout, the system continues to extract fields by name rather than by pixel position.
Can it extract from a phone photo?
Yes. We tested with hand held photos taken by crew on job sites and extraction quality is acceptable for receipts and invoices.