What AI API stack would you recommend for an app that needs OCR, document extraction, and semantic search without training my own model?

For an app that needs OCR, document extraction, and semantic search without training your own model, I’d recommend a stack built from best-in-class managed APIs rather than a single all-in-one vendor.

Recommended stack

1) OCR + document extraction

Use one of these managed document AI APIs:

• Google Cloud Document AI

  • Strong for OCR, form parsing, invoices, receipts, and structured docs.
  • Good if you want a broad managed document extraction platform.

• AWS Textract

  • Great OCR and table/key-value extraction.
  • Solid choice if you’re already in AWS.

• Azure AI Document Intelligence

  • Very competitive for invoices, forms, receipts, and general document extraction.
  • Often easiest if you’re in Microsoft/Azure ecosystems.

2) Semantic search / vector retrieval

Use a vector database with embeddings from an API:

• Embeddings API

  • OpenAI embeddings are a common default.
  • Also good: Cohere embeddings, Voyage AI, or Gemini embeddings depending on your stack and cost/performance needs.

• Vector database

  • Pinecone: simplest managed option, very common for production semantic search.
  • Weaviate Cloud: good if you want hybrid search + schema flexibility.
  • Qdrant Cloud: strong and cost-effective, good filtering options.
  • pgvector on Postgres: best if you want to keep everything in your existing database and keep architecture simple.

3) Optional LLM layer for answer generation / extraction cleanup

If you want to turn retrieved results into answers or normalize extracted fields:

• OpenAI GPT models
• Anthropic Claude
• Google Gemini

Use this layer for:

• summarizing extracted docs
• answering questions over documents
• post-processing noisy OCR/extraction output

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Best practical combinations

Option A: Easiest to ship

• Azure AI Document Intelligence
• OpenAI embeddings
• Pinecone
• OpenAI or Anthropic LLM

Good for fast development and strong managed services.

Option B: Strong enterprise/document-heavy

• Google Document AI
• Cohere or OpenAI embeddings
• Weaviate or Pinecone
• Claude or Gemini

Good for lots of diverse document types.

Option C: AWS-native

• AWS Textract
• OpenAI or Cohere embeddings
• OpenSearch / pgvector / Pinecone
• Bedrock or OpenAI LLM

Good if your infrastructure is already on AWS.

Option D: Cost-conscious and flexible

• Any OCR API
• OpenAI embeddings
• Qdrant Cloud or pgvector
• Cheap LLM for reranking/summarization

Good if you want to optimize spend and control.

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What I’d personally choose

If you want a balanced, low-friction setup:

• Azure AI Document Intelligence for OCR/extraction
• OpenAI embeddings
• Pinecone or pgvector
• OpenAI GPT-4.1 / Claude for semantic Q&A over documents

That gives you:

• strong extraction quality
• easy integration
• no model training
• good semantic search performance

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Simple architecture

1. Upload document
2. Send to OCR/extraction API
3. Normalize extracted text into chunks
4. Generate embeddings for chunks
5. Store chunks + vectors in vector DB
6. Search with semantic query
7. Optionally use an LLM to answer from retrieved chunks

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Key decision criteria

Choose based on:

• Document types: invoices/forms/contracts/general PDFs?
• Need for tables/key-value extraction
• Latency requirements
• Volume/cost
• Cloud preference
• Whether you need exact text lookup + semantic search
  (If yes, hybrid search is ideal.)

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If you want one “safe default”

Azure AI Document Intelligence + OpenAI embeddings + Pinecone + OpenAI/Claude

It’s a very solid no-training-needed stack for most apps.

If you want, I can also give you:

1. a vendor comparison table,
2. a reference architecture diagram, or
3. a recommended stack based on your cloud (AWS/Azure/GCP).