Prompt

I'm building a document extraction workflow and need a model API

Artificial Intelligence / AI Platforms1 observationLast seen Oct 9, 2026

Latest observation

Oct 9, 2026OpenAI APIWeb search: off

Absolutely — I can help with that.

For a document extraction workflow, you typically want a model API that can:

  1. Ingest documents
    PDFs, images, scans, Office docs, etc.

  2. Extract structured data
    Return JSON fields like:

    • invoice number
    • date
    • vendor
    • totals
    • line items
    • signatures
    • key clauses
  3. Handle OCR + layout awareness
    Especially for scanned docs, tables, and multi-column layouts.

  4. Support validation / schema output
    So the model returns consistent structured data instead of free text.

Good API patterns

Option A: Use a multimodal LLM API directly

Best if you want:

  • flexible extraction
  • custom document types
  • handling messy or varied layouts

You send:

  • the document text, or
  • images/pages, or
  • OCR output plus page layout hints

And ask for:

  • JSON output matching a schema

Option B: OCR + model extraction pipeline

Best if you need:

  • higher accuracy on scanned docs
  • table-heavy docs
  • cost control at scale

Typical flow:

  1. OCR the document
  2. Send OCR text + layout metadata to the model
  3. Ask for structured extraction

Option C: Specialized document AI service

Best if you mainly need:

  • invoices, receipts, IDs, forms
  • field extraction out of the box

These often include:

  • prebuilt templates
  • confidence scores
  • table parsing

What your API should support

If you’re designing or choosing one, look for:

  • JSON/schema mode
  • batch processing
  • file upload
  • page-level outputs
  • confidence scores
  • table extraction
  • citations or source spans
  • OCR fallback
  • async jobs for large docs

Example extraction request

You might structure a prompt like:

{
  "document_type": "invoice",
  "fields": [
    "invoice_number",
    "invoice_date",
    "vendor_name",
    "subtotal",
    "tax",
    "total",
    "line_items"
  ],
  "output_format": "json"
}

Example output

{
  "invoice_number": "INV-10482",
  "invoice_date": "2026-01-14",
  "vendor_name": "Acme Supplies Inc.",
  "subtotal": 1200.00,
  "tax": 96.00,
  "total": 1296.00,
  "line_items": [
    {
      "description": "Printer paper",
      "quantity": 10,
      "unit_price": 12.00,
      "amount": 120.00
    }
  ]
}

If you want, I can help with any of these next:

  • recommend a specific API/provider
  • design the JSON schema
  • write a Python/JavaScript API example
  • build a document extraction prompt
  • design a production architecture for high-volume docs

If you tell me:

  1. what document types you have,
  2. whether they’re scanned or digital, and
  3. your preferred stack,

I can suggest the best API approach.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

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Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

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Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

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