Interactive Demo | Document AI

AI Document Intelligence Workbench

Review a document queue, inspect extracted fields, trace each value back to source evidence, and decide whether the output can be approved or needs human review.

Evidence Layer

Every field points back to a source line.

For a real client, this is the part that makes document AI usable: the model output is not a black box. Each value has evidence, confidence, validation state, and a review path.

System Architecture

Production document intelligence flow.

The static demo mirrors a production implementation: files land in object storage, OCR extracts text, an LLM produces structured fields, validation gates the result, and reviewers approve exceptions.

File Intake

PDFs, scans, and images land in S3-style object storage with source metadata and intake status.

OCR + Layout

OCR captures text, tables, page coordinates, and layout blocks so extraction can point back to evidence.

LLM Extraction

A structured prompt returns normalized JSON, field confidence, source references, and unresolved values.

Schema Gate

JSON schema checks required fields, date formats, amount formats, allowed document types, and business rules.

Review Queue

Low-confidence or invalid fields route to a human reviewer with the evidence line already attached.

System Write

Approved outputs write to Postgres, trigger downstream workflow, and preserve audit lineage.

Implementation Pattern

Code-level signals.

This is the engineering layer behind the visual: typed extraction outputs, deterministic validation, and explicit review routing.

Extraction schema

{
  "document_id": "INV-1048",
  "document_type": "vendor_invoice",
  "fields": [
    {
      "name": "invoice_total",
      "value": "4825.30",
      "confidence": 0.96,
      "evidence": "Total Amount Due: $4,825.30"
    }
  ],
  "review_status": "needs_review"
}

Validation gate

for field in extraction.fields:
    schema.validate(field)
    if field.confidence < 0.88:
        route_to_review(field)

if extraction.has_required_fields() and not extraction.has_errors():
    persist_to_postgres(extraction)