Here's the exact architecture to build a secure, SOC2-compliant RAG chatbot over your firm's private case files. This pairs well with our Claude-to-Google Drive extraction pipeline for document processing.
Every law firm wants an AI assistant that actually knows their cases. Feeding confidential client documents into ChatGPT is a massive ethical violation. You need a private Retrieval-Augmented Generation (RAG) system. See personal AI agent architecture for the broader system design.
The Private RAG Architecture
Building a secure system that protects client data requires three components:
- The Embedding Engine: OCR the documents, chunk them into 500-word segments, and pass them through a model like text-embedding-3-large.
- The Vector Database: Store embeddings in a secure, self-hosted database like Pinecone or a local SQLite instance running sqlite-vss. The data stays inside your VPC.
- The LLM Call: When an attorney asks a question, query the vector database for the top 5 most relevant chunks. Inject those chunks into the prompt, and send it to Claude 3.5 Sonnet via the Anthropic API.
Zero training on your data. The Anthropic API guarantees your firm's case files never touch the training pipeline. This is the standard for legal AI.
# Example Context Injection
prompt = f"""
You are a senior paralegal. Answer the question based ONLY on the provided case files.
If the answer is not in the case files, say "I don't know."
Case Files:
{retrieved_documents}
Question: {attorney_question}
"""This is the architecture that lets you build operations you can legally trust. For related legal AI workflows, see our guides on contract review prompts and legal document automation.
Want to deploy this securely in your firm? Book a call via AI Workflow Repair Intake or check case studies for production proof.