Rebuild Your Law Firm's Client Intake with AI

Small law firms (5-30 attorneys) often rely on case management software like Clio or PracticePanther for intake. These tools provide web forms and basic email integration, but they lack intelligence. The intake forms use rigid, rule-based logic that cannot understand the unstructured text where potential clients describe their actual legal problems. This forces a paralegal or intake specialist to become a human routing system.

Consider a 10-attorney personal injury firm. An inquiry arrives via their website's contact form. The email contains a two-paragraph description of an auto accident and a scanned police report as a PDF. A paralegal must first read the email to determine the case type. Then, they must download, open, and read the PDF to find the date of incident, names of parties involved, and the reporting officer's name. Finally, they manually create a new matter in Clio, copy-pasting this information into the correct fields and assigning it to an attorney. This entire sequence takes 10-15 minutes of skilled time for every single inquiry.

The structural failure is that these platforms are databases with forms, not language processing systems. They are designed to store structured data, not to create it from unstructured sources like an email or a scanned document. They cannot classify intent, extract specific entities like names or dates from a paragraph, or summarize a narrative. Any automation they offer, like assigning all inquiries from a specific form to one person, is too coarse to be truly useful.

The result is a permanent bottleneck. Attorney time is wasted on administrative setup, response times to potential clients lag, and the risk of data entry errors that affect case files is constant. The firm pays for skilled paralegal hours to be spent on repetitive data transcription, a task that is a poor use of their training and the firm's money.

Syntora would begin with a discovery process to map your firm's exact intake workflow. We would identify all your case types, the key pieces of information required to qualify each one, and where that information typically lives in an initial client email or attached documents. This audit produces a clear data schema for what the AI needs to extract, forming the blueprint for the build. You receive a scope document detailing this logic before any code is written.

The core of the system would be a series of AWS Lambda functions orchestrated by FastAPI. When an email arrives at your intake address, a function triggers. It uses the Claude API to read the email body and any attachments. For PDFs, an OCR process converts the document to text first. Claude then performs three tasks: it classifies the matter into a predefined category (e.g., 'Auto Accident,' 'Slip and Fall'), extracts key entities (names, dates, locations), and generates a concise summary. All this structured data is then pushed into a Supabase database which serves as an auditable staging area.

The delivered system provides a simple web interface for a paralegal to review the AI's work. For the first 100 cases, every classification and extraction requires human approval. This human-in-the-loop process allows for fine-tuning and builds trust. Once accuracy exceeds a 98% threshold, the system can be switched to fully automatic mode, creating new matters in your case management system via its API. The data remains on your infrastructure, and the full Python source code is delivered to you.