Build a Custom AI Assistant for Legal Document Drafting

Solo practitioners often try using the AI features within large platforms like Lexis+ AI or Westlaw AI. These tools are powerful for research but their drafting assistants are trained on generic legal corpora. They might suggest a 'market standard' clause that directly contradicts a specific, hard-won term you use for all your clients, forcing you to manually correct the AI's work.

A common scenario involves a solo real estate attorney reviewing a commercial lease from opposing counsel. The practitioner knows their standard indemnity clause is superior. They must stop, open their own template file, find the correct clause, and copy-paste it into the draft. This manual check-and-replace process happens 5-10 times per document, consuming 30 minutes of non-billable time and introducing risk if the wrong version is pasted.

Contract lifecycle management (CLM) platforms like Ironclad are designed for large in-house legal teams, not solo practitioners. Their 'AI' is often a set of rigid rules, and you pay a high monthly fee for features you'll never use, like multi-stage approval workflows. Sending client data to a public tool like ChatGPT presents major confidentiality and security risks, and the model cannot access your private work product to ensure consistency.

The structural problem is that all these tools are one-size-fits-all. A solo practitioner's primary value is their specialized knowledge, captured in their own documents. Off-the-shelf software cannot be trained on a small, private library of a few hundred documents. Their architecture is built for mass-market scale, not individual expertise.

The process would begin with an audit of your existing document library. We would identify 50-100 key documents representing your best work: executed contracts, final templates, and your private clause library. This curated set of documents becomes the ground truth for the AI, ensuring its outputs reflect your specific legal reasoning and style. This audit defines the scope and confirms the source material is ready.

The technical approach would be a retrieval-augmented generation (RAG) system. Your clause library would be converted into vector embeddings and stored in a Supabase Postgres database using the pgvector extension. When you submit a new draft, a FastAPI application queries this database for semantically similar clauses from your work, passing them as context to the Claude API. This grounds the AI's analysis in your firm's actual language, not generic text. All data remains on your client infrastructure, hosted on AWS.

The delivered system is a secure web interface where you upload a document. Within 60 seconds, the application returns an annotated version highlighting non-standard clauses and suggesting replacements from your library, complete with explanations. You receive the full source code deployed in your AWS account and a runbook explaining how to add new documents to the knowledge base as your practice evolves.