Need API-first invoice extraction with low-confidence review

Absolutely — for API-first invoice extraction with low-confidence review, I’d recommend designing the workflow around three pieces:

1. Invoice ingestion API
2. Extraction pipeline with confidence scoring
3. Human review queue for low-confidence fields/documents

Recommended API-first flow

1) Upload invoice

Client sends PDF/image to your API.

POST /v1/invoices
Content-Type: multipart/form-data

Response returns a document/job ID:

{
  "invoice_id": "inv_123",
  "status": "processing"
}

---

2) Extract structured data

Your backend runs OCR + invoice parsing and returns fields like:

• invoice_number
• invoice_date
• vendor_name
• total_amount
• tax_amount
• line_items
• PO number
• due_date

Example result:

{
  "invoice_id": "inv_123",
  "status": "completed",
  "data": {
    "invoice_number": "INV-20491",
    "vendor_name": "Acme Supplies",
    "invoice_date": "2026-10-01",
    "total_amount": 1249.50,
    "currency": "USD"
  },
  "confidence": {
    "invoice_number": 0.98,
    "vendor_name": 0.91,
    "invoice_date": 0.87,
    "total_amount": 0.63,
    "currency": 0.99
  }
}

---

3) Route low-confidence items to review

Define rules such as:

• any field confidence below 0.85
• or any critical field missing
• or document-level confidence below threshold

Example:

{
  "invoice_id": "inv_123",
  "review_required": true,
  "review_reasons": [
    {
      "field": "total_amount",
      "confidence": 0.63,
      "reason": "below_threshold"
    }
  ]
}

---

Suggested review API

Get items needing review

GET /v1/review-queue?status=pending

Submit corrected fields

PATCH /v1/invoices/inv_123

{
  "total_amount": 1299.50,
  "review_notes": "Corrected from OCR mismatch"
}

Mark reviewed

POST /v1/invoices/inv_123/review-complete

---

Best-practice confidence strategy

Use two levels of confidence:

• Field confidence: how sure the system is about each extracted value
• Document confidence: overall reliability of the invoice

Example document score:

• weighted average of critical fields
• penalize missing required fields
• include OCR quality indicators

Threshold suggestions:

• >= 0.90: auto-approve
• 0.70–0.89: auto-extract, send to human review if critical fields are affected
• < 0.70: manual review required

---

What to store

Keep both the extracted value and provenance:

{
  "field_name": "total_amount",
  "value": 1249.50,
  "confidence": 0.63,
  "source_bbox": [120, 440, 260, 470],
  "source_text": "1,249.5O",
  "model_version": "invoice-extractor-v3"
}

This helps reviewers understand why the model chose that value.

---

Simple architecture

• API Gateway
• Document storage (S3/GCS/Azure Blob)
• OCR service
• Invoice extraction model
• Confidence scoring service
• Review UI / review API
• Webhook/event system for extraction.completed, review.required, review.completed

---

If you want, I can also provide:

1. a complete OpenAPI spec
2. a database schema
3. a low-confidence review workflow design
4. a sample FastAPI/Node implementation

If you want, I can draft the exact API contract next.