Automate Legal Client Intake With Custom AI

Many 15-person law firms rely on case management systems like Clio or MyCase for intake. These tools provide web forms that capture structured data, but their automation stops there. When a potential client emails a set of documents, a paralegal must manually open, read, and interpret everything before creating a new matter. This process is entirely manual and consumes hours of valuable staff time.

Consider a personal injury firm receiving an email with a 15-page police report and 30 pages of initial medical records as PDFs. The intake paralegal spends 20-30 minutes just reading to identify the parties, date of incident, and summary of injuries. They then swivel-chair to PracticePanther, manually re-typing this information to create a new matter, and finally upload the original documents. This happens 10 times a day, introducing a high risk of data entry error on critical details like filing deadlines.

A tech-savvy firm might try to connect Gmail to Clio with an integration tool. This fails because the trigger is too simple, firing on every email regardless of content. These platforms cannot read a PDF to distinguish a new client inquiry from a court notice or a marketing email. The conditional logic needed to classify and route documents based on their content requires an AI model, which is a capability these tools fundamentally lack.

The core problem is that case management systems are designed as databases for record-keeping, not as content analysis engines. Their architecture is optimized for storing structured information, not for extracting it from unstructured sources like PDFs and emails. This creates a permanent gap between where information arrives (email) and where it needs to live (the case file), a gap that can only be closed by manual labor or a custom AI system.

The project would begin with an audit of your firm's current intake process. We would analyze the last 100 client inquiries to understand the common document types, data points you need to extract, and the routing logic for different matter types. This discovery phase produces a detailed specification document that maps out the entire data flow, from email arrival to matter creation in your case management software.

The technical solution would use an AWS S3 bucket to receive email attachments securely. An AWS Lambda function would trigger on a new document, performing OCR if needed and passing the text to the Claude API for analysis. Claude would be prompted to extract entities like plaintiff names, incident dates, and matter type, returning structured JSON with over 95% accuracy. A custom FastAPI service would then use this data to connect to your case management API, creating the new matter in under 5 seconds.

The delivered system is a private, secure pipeline that runs on your firm's own cloud infrastructure, typically costing under $50 per month to operate. Syntora provides the complete Python source code, a runbook for maintenance, and an audit trail for every document processed. A human-in-the-loop interface allows a paralegal to review and approve the AI's classification for sensitive cases before the matter is formally created, ensuring full control and compliance.