Obsurfable
Sign inCreate free account

Prompt

I'm building a document extraction app and need an LLM API

Artificial Intelligence / AI Platforms1 observationLast seen Oct 8, 2026

Latest observation

Oct 8, 2026OpenAI APIWeb search: off

Absolutely — for a document extraction app, you’ll usually want an LLM API that can do one or more of these well:

  • OCR-aware text extraction from scanned PDFs/images
  • Structured data extraction into JSON
  • Classification / routing of document types
  • Chunking and summarization for long docs
  • Tool/function calling to validate and normalize fields

Good API options

Here are the common choices:

1) OpenAI API

Best if you want:

  • strong general-purpose extraction
  • structured JSON outputs
  • robust function calling / tool use
  • multimodal support for images/PDF workflows

Useful when you need:

  • invoice/receipt/contract field extraction
  • schema-constrained outputs
  • high-quality reasoning over messy documents

2) Anthropic API

Best if you want:

  • strong long-context document processing
  • high-quality text understanding
  • good extraction from long contracts and reports

3) Google Gemini API

Best if you want:

  • multimodal document understanding
  • strong integration with Google Cloud ecosystem
  • large-context processing

4) AWS Textract + LLM

Best if you need:

  • purpose-built OCR and form/table extraction
  • hybrid pipeline: OCR first, LLM second
  • enterprise document workflows

5) Azure Document Intelligence + Azure OpenAI

Best if you want:

  • enterprise document extraction
  • OCR/layout parsing plus LLM post-processing
  • Microsoft ecosystem integration

Recommended architecture for document extraction

For most apps, the best pipeline is:

  1. Detect document type
  2. Run OCR / layout parsing if needed
  3. Send text + layout metadata to LLM
  4. Ask for structured JSON output
  5. Validate against schema
  6. Fallback / retry if confidence is low

What to look for in an API

You’ll want an API with:

  • Structured outputs / JSON mode
  • Vision support if you have scanned docs or images
  • Long context window for big documents
  • Low latency
  • Batch processing
  • Reliable billing and rate limits
  • Data privacy / retention controls

If you want a simple recommendation

  • Best overall: OpenAI API
  • Best for OCR-heavy enterprise docs: Azure Document Intelligence or AWS Textract + LLM
  • Best for long contracts / huge docs: Anthropic or Gemini

If you want, I can help you choose the best API based on:

  1. your document types,
  2. whether you need OCR, and
  3. your preferred stack (Python, Node, etc.).

Brands

Openai ApiAnthropic ApiGoogle Gemini ApiAws TextractAzure Document IntelligenceAzure Openai

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.

How can brands improve their AEO?

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.

Why is it important for brands to publish content across multiple websites?

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.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.