I'm building a document app that needs API-first extraction from PDFs and emails

That makes sense. For a document app with API-first extraction from PDFs and emails, the main goal is usually to turn unstructured documents into clean structured data reliably and fast.

Here’s a practical way to think about it:

Core extraction capabilities

You’ll likely want separate pipelines for:

1) PDF extraction

Support:

• Text-based PDFs
• Scanned/image PDFs
• Mixed-layout PDFs with tables, headers, footers
• Attachments inside PDFs if relevant

Typical outputs:

• Raw text
• Page-level text
• Structured blocks: paragraphs, tables, key-value pairs
• Metadata: filename, page count, author, created date
• Coordinates/bounding boxes for traceability

2) Email extraction

Support:

• Subject, sender, recipients, CC/BCC
• Message body
• HTML and plain text
• Attachments
• Threaded conversation handling
• Signature and quoted text cleanup

Typical outputs:

• Clean body text
• Structured headers
• Attachment objects
• Conversation thread metadata

---

API-first design

A good API design usually includes:

Upload / ingest

• POST /documents for PDFs
• POST /emails for raw email files or MIME content
• POST /extract for synchronous extraction if the file is small

Async processing

For larger docs, use:

• POST /jobs
• GET /jobs/{id}
• webhook callback on completion

Retrieval

• GET /documents/{id}
• GET /documents/{id}/extraction
• GET /documents/{id}/pages/{page}

---

Recommended extraction architecture

A solid pipeline looks like:

1. Ingest
   • Accept PDF, EML, MSG, raw MIME, or uploaded file
2. Classify
   • Detect whether it’s text PDF, scanned PDF, or email
3. Parse
   • Extract raw text and structural elements
4. Normalize
   • Clean whitespace, headers/footers, quote blocks, signatures
5. Enrich
   • Detect entities, dates, amounts, addresses, participants
6. Return structured JSON
   • Make it easy for the app to index, search, and display

---

Important product decisions

You should decide early:

• Do you need OCR? Essential for scanned PDFs and image-based emails.
• Do you need table extraction? Many business documents depend on this.
• Do you need layout preservation? Important for legal, finance, and compliance use cases.
• Do you need confidence scores? Very useful for downstream review workflows.
• Do you need multi-tenant isolation? Important for SaaS apps.
• Do you need document-to-JSON schemas? Great for invoices, contracts, claims, etc.

---

Suggested response format

For API consumers, a response like this is usually very useful:

{
  "document_id": "doc_123",
  "type": "pdf",
  "pages": 12,
  "text": "extracted full text...",
  "blocks": [
    {
      "page": 1,
      "type": "paragraph",
      "text": "..."
    }
  ],
  "metadata": {
    "filename": "example.pdf",
    "created_at": "2026-10-11T12:00:00Z"
  },
  "entities": [
    {
      "type": "date",
      "value": "2026-10-01",
      "confidence": 0.98
    }
  ]
}

For emails:

{
  "message_id": "msg_123",
  "subject": "Invoice follow-up",
  "from": "alice@example.com",
  "to": ["bob@example.com"],
  "body_text": "Hi Bob...",
  "attachments": [
    {
      "filename": "invoice.pdf",
      "mime_type": "application/pdf"
    }
  ]
}

---

If you want to go further

I can help you design any of these next:

1. A full API spec
2. A JSON schema for extracted PDFs/emails
3. A pipeline architecture
4. A tech stack recommendation
5. A prompt/template for LLM-based extraction
6. A production-ready system design for scale

If you want, I can draft a concrete OpenAPI spec for your extraction API next.