Secure Vector-LLM Legal Assistant (legal experience only)

I need a developer who can wire a private vector store to either use OpenAI’s API and Anthropic’s Claude API so I can question my own directory of case law and statutes without anything leaking beyond my walls. Speed matters: queries should return in seconds, even when the document set grows. Security is non-negotiable, and the architecture must scale as I add more files or swap to bigger cloud instances.

Here’s the end-to-end flow I’m after: I drop new PDF opinions or briefs into a directory, an ingestion routine chunks and embeds them, the embeddings land in a secure vector database, and a slim UI (or endpoints I can call from my legal system) lets me ask a question. The system then composes a response—whether it is an email to opposing counsel, a client letter, or material for a formal legal brief—citing the sources it pulled from my corpus.

Essential pieces I expect from you:
• Infrastructure code (Docker, Terraform or similar) that spins up the vector database with proper encryption at rest and in transit
• Ingestion script that handles common legal file types (PDF, DOCX, text) and keeps embeddings in sync when I update or delete a file
• Query API or lightweight web front end with auth, model toggle, and streaming answers
• Clear instructions so I can redeploy or extend the stack myself

Acceptance criteria
1. Query latency under 3 s on a 10 k-document test set
2. End-to-end encryption verified by independent tools (provide commands or screenshots)
3. Citations in every response linking back to the exact source file and page/paragraph
4. Successful generation of an email, a client letter, and a brief excerpt from the same prompt during demo

If you have prior work with secure RAG pipelines, let’s talk.