Document AI SaaS products are in massive demand. Lawyers analyzing contracts, accountants reviewing financial statements, researchers summarizing papers, HR teams processing resumes — every knowledge worker deals with documents daily. Here's how to build the technical foundation.
The Technical Architecture
A document AI system has four layers:
- Ingestion — accept document uploads (PDF, DOCX, TXT, CSV)
- Processing — extract text from documents
- Chunking and Embedding — split text into chunks, generate vector embeddings
- Query and Generation — accept user questions, retrieve relevant chunks, generate answers
PDF Text Extraction
For PDFs, use the pdf-parse npm library to extract text. For scanned PDFs (images, not text), you need OCR — use AWS Textract or Google Document AI via their APIs. Store extracted text in Supabase alongside the original file reference.
Text Chunking Strategy
Split extracted text into overlapping chunks of 500–1,000 tokens. Overlapping chunks (50–100 token overlap) ensure that context isn't lost at chunk boundaries. Generate embeddings for each chunk using text-embedding-3-small and store in Supabase pgvector.
The Q&A Interface
When a user asks a question:
- Generate an embedding for the question
- Search pgvector for the most similar document chunks (top 5–10)
- Pass retrieved chunks + question to GPT-4o: "Based on this document content, answer: [question]"
- Stream the response back to the user
Show Citations
Always show which part of the document an AI answer came from. Display the relevant text snippet with a "Jump to page X" link. This is the single feature that makes document AI trustworthy — users can verify the AI's answer against the source.
Build Your Document AI SaaS
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Start on Fiverr →Monetization
Charge per document processed (credit model) or per workspace (subscription). $49/month for 20 documents/month, $149/month for 100 documents. Professional law firms and consulting companies will pay $299–499/month for enterprise plans with higher limits and team access.
Scaling Document Processing
Document AI features need background job infrastructure from day one. Processing a 50-page PDF synchronously in an API route will time out and frustrate users. Queue document processing jobs with a system like Inngest, BullMQ, or Trigger.dev — accept the document upload immediately, return a job ID, process asynchronously, and notify the user when processing is complete via email or a real-time UI update. This architecture handles large documents gracefully, retries on failure without user intervention, and scales horizontally as document volume grows.