Automate Your Legal Client Intake with a Custom AI System

Many solo practitioners rely on the intake features of their Legal Practice Management Software (LPMS) like Clio Grow, MyCase, or PracticePanther. These tools provide web forms that capture structured data like names and phone numbers. They fail, however, the moment unstructured documents enter the workflow. The automation is rigid, rule-based logic that cannot intelligently interpret the content of a PDF attachment.

Consider a solo family law attorney. A potential client fills out the website form but also emails three critical PDFs: a prior divorce decree, a recent W-2, and a child custody agreement. The LPMS logs the new contact from the form data but does nothing with the attachments. The attorney must still manually download each file, open it, read it to understand its context, give it a standardized file name, and upload it to the correct folder in the matter. This is 20 minutes of non-billable administrative work before even deciding whether to take the case.

Third-party document tools can perform OCR, but they are not integrated into the legal workflow. You end up with extracted text in a separate system, forcing you to copy and paste information back into your LPMS. This fragmentation increases the risk of data entry errors and breaks the flow of information. You are left managing multiple tools that do not communicate, defeating the purpose of automation.

The structural problem is that LPMS platforms are built to be systems of record, not dynamic processing engines. Their architecture prioritizes billing and structured data storage, not the complex, document-centric analysis required for legal intake. They cannot be customized to understand the specific documents relevant to your practice area, forcing you to bridge the gap with manual labor.

The engagement would begin with a thorough audit of your current intake process. Syntora would map every step, from the first client email to a confirmed consultation, and catalog every type of document you receive. You would receive a detailed process diagram that serves as the blueprint for the automation, ensuring the final system perfectly matches your firm's workflow.

The core of the system would be a Python service built with FastAPI, deployed on AWS Lambda for cost-effective, serverless execution. When a new client email arrives, a webhook triggers the service. The system uses an OCR library to extract raw text from PDF attachments and sends it to the Claude API. Using specific instructions, Claude classifies the document (e.g., 'Financial Affidavit', 'Prior Court Order'), extracts key entities like case numbers or asset values, and generates a 150-word summary. The architecture is designed to handle up to 500 intake documents per month for under $50 in total cloud costs.

The extracted data, summaries, and original documents would be automatically filed into the correct client matter in your LPMS via its API. Simultaneously, the system would check your calendar for available consultation slots and email the client a scheduling link. The entire process, from email receipt to the client booking a meeting, would complete in under 60 seconds. A full audit trail of every action is stored in a Supabase database for compliance and review.