Custom Legal AI for Your 5-Attorney Practice

Most small law firms run on practice management software like Clio or PracticePanther. These tools are excellent databases for matter management, contacts, and billing. They are not, however, intelligent processing engines. A document uploaded to Clio sits there as a static file. The software cannot read its contents, classify it as a deposition versus a motion, or extract key dates and entities to summarize for an attorney.

Consider a 5-attorney personal injury firm. When a paralegal receives a 150-page medical record PDF via email, the process is entirely manual. They must open the file, read through every page to identify providers and treatment timelines, write a summary, create a new matter in Clio, upload the document, and then manually notify the responsible attorney. This single task consumes 45 minutes of skilled time and is susceptible to human error. If a key detail is missed, it can impact case strategy.

This gap exists because practice management platforms are architected for structured data entry, not for unstructured document analysis. Their business model is to be a system of record, so they lack the event-driven architecture needed to trigger a multi-step AI pipeline. Off-the-shelf AI legal tools are often priced for large firms with multi-year, five-figure contracts and require you to send sensitive client data to their cloud, which is often a non-starter for firms with strict data residency and privacy requirements.

The first step is a workflow audit. We would sit with your team and map a single, high-value process like new client intake or medical record review. We would analyze sample documents to understand their structure and define the exact data points that need to be extracted. This discovery phase results in a clear project specification that you approve before any build work begins.

The technical approach would center on a secure FastAPI service running on your private infrastructure. When a document arrives in a designated AWS S3 bucket, it triggers the service. The Claude API reads and understands the document, extracts the specified information, and generates a summary. A Supabase PostgreSQL database would store an immutable audit trail of every action taken. This architecture ensures sensitive client data is processed in your environment, not on a shared third-party platform.

The delivered system would integrate directly into your current process. For example, a paralegal could forward an email with a PDF attachment to a specific address. Within 90 seconds, they and the assigned attorney would receive a secure message with the extracted summary and a link to a pre-filled draft matter in Clio. A human-in-the-loop gate ensures an attorney always reviews and approves the AI-generated summary before it becomes part of the official case file.