I'm building a SaaS product and want an AI API for search, extraction, and generation

That’s a solid use case for an AI API. For a SaaS product, you’ll usually want an API that can handle three core jobs well:

1. Search / retrieval
2. Extraction / structuring
3. Generation / summarization / answering

A good way to think about it is: retrieve the right information, turn it into clean data, then generate a response.

What to look for in an AI API

1) Search

If your product needs to find relevant content from documents, knowledge bases, or the web, look for:

• Embeddings for semantic search
• Reranking to improve relevance
• Vector search integration or support for your vector DB
• Long-context models for direct document Q&A when retrieval isn’t enough

2) Extraction

For SaaS workflows, extraction is often the most valuable capability. You’ll want an API that can:

• Pull structured fields from unstructured text
• Return JSON / schema-constrained outputs
• Extract entities, dates, totals, names, IDs, etc.
• Handle OCR’d text or mixed-format docs if you work with PDFs/images

3) Generation

For customer-facing features, generation typically includes:

• Chat and Q&A
• Summaries
• Drafting emails, reports, or notes
• Task-specific content generation with guardrails

Architecture pattern that works well

A common production setup is:

1. Ingest data
2. Chunk and embed
3. Store in vector DB
4. Retrieve top results
5. Optionally rerank
6. Extract structured data or generate answer
7. Post-process and validate outputs

What matters for SaaS

Since you’re building a product, not just a demo, you should evaluate:

• Latency
• Cost per request
• Structured output support
• Tool/function calling
• Batch processing
• Rate limits
• Data privacy and retention
• Observability / logs
• Model quality on your specific domain

If you want a single API provider

You may prefer a platform that offers all three:

• Strong search/retrieval support
• Reliable structured extraction
• High-quality generation models

That way you can keep your stack simpler.

Common implementation combo

A very practical stack is:

• Embeddings model for search
• Vector database like Pinecone, Weaviate, Milvus, pgvector, etc.
• LLM for extraction and generation
• JSON schema / function calling for reliable outputs

Example SaaS use cases

• Contract analysis
• Support ticket triage
• CRM enrichment
• Lead intelligence
• Document understanding
• Knowledge base assistant
• Meeting notes and action items
• Invoice / receipt processing

If you want, I can help you narrow this down to:

1. the best API providers for this,
2. a recommended architecture, or
3. a sample implementation in Python/Node.