Automate Your Firm's Client Intake with a Custom AI System

Many small law firms rely on practice management software like Clio or MyCase. These tools are excellent for managing structured case data once it exists, but they struggle with the messy, unstructured reality of client intake. Their built-in intake forms are often rigid, and their automation features typically depend on pre-defined rules that cannot interpret the content of an attached police report or a scanned medical record.

Consider a 15-person personal injury firm. A paralegal's morning involves monitoring an inbox flooded with new inquiries. Each email contains a mix of scanned intake forms, photos, and PDFs of varying quality. For every new client, the paralegal must open multiple attachments, manually find and re-type the client's name, date of incident, and contact information into the case management system. This process takes 20-30 minutes per client and is prone to data entry errors.

The core issue is that existing legal software is built around databases, which require structured input. These systems cannot perform Optical Character Recognition (OCR) on a fuzzy scan or use a language model to understand that "the accident on I-5" refers to the incident location. The software can't read; it can only store what a human has already read and typed. This architectural limitation forces your highly-trained staff into low-value data entry work.

The result is a bottleneck. Attorneys wait for paralegals to process the intake queue, clients wait longer for a response, and the firm pays for skilled staff to perform repetitive copy-paste tasks. The risk of a critical detail being missed or entered incorrectly during manual transcription is a constant, low-level threat to the quality of service.

Syntora would begin with a two-day audit of your firm's current intake workflow. We would analyze a sample of 25-50 recent client intake packages to identify all document types, data fields, and variations. This audit produces a clear data map and a technical plan that shows exactly how the system would handle your specific documents before any code is written.

The technical approach would be a serverless pipeline on AWS. An incoming email with attachments would trigger an AWS Lambda function. This function saves the documents to a secure AWS S3 bucket. Another function would use an OCR engine to convert image-based PDFs into text. That text is then passed to the Claude API, which extracts key entities (names, dates, locations, injury descriptions) and generates a 200-word summary. All extracted data, the summary, and an audit trail are stored in a Supabase database.

The delivered system would be a FastAPI application providing a secure dashboard for your paralegals. Instead of an inbox, they would see a queue of new matters with the AI-generated summary and extracted data ready for a 1-click verification. Once verified, the data is pushed to your existing case management software. This human-in-the-loop gate ensures an attorney always gives the final approval, maintaining professional oversight while automating 90% of the manual work. The entire process for a typical case with 3-4 documents would take less than 90 seconds.