Automate Personal Injury Lead Qualification with Voice AI

Standard phone systems and IVRs fail at lead qualification because they only route calls. They cannot understand a caller's intent or ask follow-up questions. A potential client who says, "I was rear-ended and my back hurts," is put on hold just like a vendor calling about an unpaid invoice. This delay often results in the lead calling the next law firm on Google.

Entering a case into a system like Filevine or Clio requires specific information: date of injury, treatment status, and police report details. Off-the-shelf voice bots from services like RingCentral are not designed to capture this structured legal data. They can book an appointment, but they cannot fill out the 12 custom fields your intake team needs to evaluate a case, leading to staff spending time re-interviewing the caller.

Platforms like Voiceflow or Botpress provide powerful tools but are not a complete solution. A law firm needs an engineer to design the conversational logic, integrate with a language model API, deploy the system, and connect it to a legal CRM. These platforms often use per-minute pricing that becomes expensive for the 5-10 minute calls common in PI intake, and their pre-built CRM connectors are rarely compatible with legal-specific software.

Syntora would approach this problem by first translating your firm's exact intake script into a state machine, managed within a Python application. For conversational logic, we specify the Claude API due to its strong instruction-following capabilities, which are effective for handling tangents and re-asking questions naturally. This design approach allows for clear control over the conversational flow, often preferred over visual builders for complex logic.

The voice interface would be constructed on AWS infrastructure. We would use AWS Polly for text-to-speech and AWS Transcribe for real-time speech-to-text. This combination is chosen to achieve low latency, contributing to a natural-feeling conversation. Call streaming would be managed by connecting the system to a dedicated phone number using the Twilio API. The application would be deployed using AWS Lambda, which allows for cost-effective scaling to accommodate varying call volumes.

Following each call, the complete transcript and a Claude-generated JSON summary would be stored in a Supabase database. This summary is engineered to extract key fields such as `injury_date`, `at_fault_party`, and `is_getting_treatment`. This structured data would then be pushed to your firm's CRM, such as Clio or Filevine, via a direct API integration. The objective is to automate the creation of a new contact and matter, streamlining your intake process.

For monitoring and quality control, the system would incorporate `structlog` to create detailed logs for every call. Should the AI fail to generate a valid case summary after a defined number of attempts, a Slack alert containing the full call transcript would be sent to your intake team for immediate manual review. This mechanism ensures that potential lead issues are flagged promptly.