AI Document Processing & OCR Solution

I need a compact, cloud-ready system that lets users drop one or many PDFs, have them OCR-processed, embedded for vector search, and then converse with the contents through a fast, text-based chat window. “Chat with documents” is the very first milestone and must feel as natural as Slack or Teams: type a question, receive a concise answer that quotes the relevant page ranges and links straight back to the source.

Once that core loop is rock-solid, the same processing pipeline should expose:
• high-accuracy OCR for scanned pages,
• field-level data extraction that can be fine-tuned per document type, and
• a clean JSON export endpoint so downstream systems in law firms or finance teams can consume the results automatically.

Expected flow
1. User uploads PDF(s).
2. Service performs OCR where needed.
3. Embeddings are generated and stored (FAISS, Pinecone, or similar—your choice as long as latency stays under two seconds for typical 100-page bundles).
4. A web chat box accepts text input and streams answers back, citing sources.
5. An API call returns the full extracted data set in JSON.

Acceptance criteria
• Chat responses ≤2 s on 100-page test set.
• OCR accuracy on supplied samples ≥95 % character level.
• JSON schema exactly as provided after contract award.
• All code handed over, installable with one-command Docker compose.

Tech is flexible—Python, Node.js, LangChain, LlamaIndex, OpenAI or open-source LLMs are fine as long as licensing remains enterprise-friendly. Security, logging, and a clear README are mandatory because the target users handle sensitive financial and legal documents.

Ready to start as soon as you can outline your proposed stack and timeline.