Automate Client Intake for Your Small Law Firm with AI

Small firms often rely on their Practice Management System like Clio or MyCase for intake. These systems have web forms, which work for structured data collection. The real work starts when a client emails a 50-page PDF of financial statements or a prior court filing. The intake paralegal must manually read these documents, identify key dates and entities, and copy-paste them into the correct fields in Clio.

Consider a 3-attorney family law practice. A new client emails a zip file containing bank statements, a separation agreement draft, and a property deed. The paralegal spends 45 minutes opening each PDF, finding the client's name, spouse's name, date of marriage, and property address, then re-typing it all to create a new matter in PracticePanther. If a number is transcribed incorrectly, it creates errors downstream that can impact filings.

The core problem is that systems like Clio are databases with a user interface, not data processing engines. Their architecture is built for storing structured data that humans enter, not for ingesting and understanding unstructured documents. They lack OCR capabilities and have no built-in connection to language models that can parse legal text. They provide the destination for the data but offer no help getting it there.

This manual bottleneck means billable attorney time is sometimes spent on administrative work, or paralegal time is consumed by low-value data entry. It introduces a high risk of human error in critical case details and delays the start of substantive legal work. The firm's capacity is capped by how fast its staff can type, not by how well its attorneys can practice law.

The engagement would begin by auditing your current intake process. Syntora would review a sample of 20-30 client-submitted documents across your main practice areas to identify the key data points you need to extract. We would also map the specific fields in your case management system to ensure the extracted data lands in the right place.

A proposed system would use an AWS S3 bucket to receive documents via email. An AWS Lambda function would trigger on each new file, using Amazon Textract for OCR. The text would then be passed to the Claude API to extract entities like names, dates, and case numbers with a target latency of under 5 seconds per document. This data would be validated using Pydantic schemas and then inserted into a Supabase backend for staging and review. A build for this would typically take 4-6 weeks.

The final system runs entirely on your own cloud infrastructure, ensuring data privacy. Your team's workflow changes minimally: they forward attachments to a dedicated intake email address. A human-in-the-loop interface allows a paralegal to review the AI's extractions and approve them with a single click before the case file is created, providing a critical audit trail. The system could process over 1,000 documents per month for less than $50 in cloud hosting fees.