Automate Client Intake and Screening for Your Law Firm

Many small law firms managing personal injury, real estate, or debt collection matters rely on practice management software like Clio, MyCase, or PracticePanther for intake. While these platforms excel as systems of record and for structured case management, their automation capabilities are often limited to rule-based triggers. They can send a predefined email when a form is submitted, but they lack the intelligence to interpret an unstructured email from a potential client, understand the context of an attached contract, or route a complex inquiry to the right specialist.

Consider a 15-attorney firm handling a mix of commercial litigation and intellectual property. Incoming inquiries arrive as emails with attachments ranging from scanned patent applications to draft agreements. A paralegal must manually open each attachment, determine the matter type, extract key entities like parties and deadlines, and then manually create a new matter in PracticePanther. This often involves downloading a 15-page PDF for a draft contract review, identifying it as a specific matter type, flagging non-standard clauses against a firm's internal clause library, and then creating a summarized task for an attorney. Each of these manual steps, repeated dozens of times a day, is non-billable and introduces delays in client response.

Firms sometimes attempt to bridge this gap with siloed Python automation scripts run as standalone EXEs on individual developer workstations or basic no-code tools for webform connections. However, these custom scripts often lack centralized code management and formal code review processes, creating compliance risks and making them brittle when faced with pagination bugs in email scrapers that miss high-volume spikes. This fragmented approach also fails when presented with truly unstructured documents that require advanced OCR and AI interpretation to classify, summarize, and extract critical information for efficient routing. The absence of a dedicated processing layer means the 'system of record' cannot become a 'system of intelligence' for intake.

Syntora engineers solutions that integrate directly into your firm's operational workflow, extending the capabilities of existing practice management systems. The engagement would begin with a detailed audit of your current intake workflow for each practice area. We would map how new inquiries arrive (email, web form, physical document scans), what specific information is needed to qualify them, and the precise logic for routing them to the correct attorney or department. This discovery phase produces a data flow diagram, an architectural blueprint, and a firm-specific classification guide, all of which you would approve before any build commences.

The core of the system would be a Python-based FastAPI service deployed within your firm's own AWS account, ensuring data residency and security. When a new email arrives at your intake address, an AWS Lambda function would trigger the process. The system would use an OCR service to extract text from any PDF attachments, including complex legal documents like draft contracts or court filings. This extracted text is then sent to the Claude API, which would perform multiple tasks: classifying the matter type (e.g., 'commercial lease review,' 'personal injury initial consultation'), summarizing the inquiry, and extracting key entities like names, dates, and non-standard contract clauses. We have built document processing pipelines using Claude API for financial documents, and the same architectural pattern applies directly to legal document intake and review.

The delivered system would connect directly to your practice management software's API (e.g., Clio, MyCase, PracticePanther, or even JST CollectMax for debt collection firms). It would automatically create a new contact and matter, populate it with the AI-generated summary, attach the original documents from a secure AWS S3 bucket, and assign an initial task to the correct attorney or paralegal. Any inquiry the AI flags as ambiguous or potentially high-risk would be routed to a human review queue, ensuring an attorney always has the final say (human-in-the-loop). All AI decisions would be logged with confidence scores to an audit trail, and the entire code base would be managed with GitHub Actions CI/CD, incorporating CODEOWNERS-style required reviewer gates. Data would remain on your client infrastructure, secured by Okta MFA.

Typical build timelines for a system of this complexity range from 6 to 10 weeks, depending on the number of practice areas and integration points. You would receive the full source code, comprehensive documentation, and a maintenance runbook, ensuring complete ownership and control. Clients provide access to existing systems, sample intake documents, and detailed classification examples.