Automate Personal Injury Lead Qualification with Voice AI
The best voice AI solution for automating personal injury lead qualification is a custom-built system. It uses a large language model to ask targeted intake questions over the phone.
This system captures key case details and creates a structured summary for your intake team. The scope depends on the complexity of your intake script and CRM integration. A simple motor vehicle accident screener that creates a contact in Clio is a 3-week build. A system with branching logic for MVA, slip-and-fall, and medical malpractice cases that books qualified leads into Acuity requires more upfront design.
We built an intake system for a 7-person PI firm handling 40 new calls per day. Their two intake specialists spent 15-20 minutes on each call. The AI now handles the initial screening in under 2 minutes, and the team only calls back pre-qualified leads with complete case details already in their CRM.
What Problem Does This Solve?
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.
How Does It Work?
We start by translating your firm's exact intake script into a state machine managed in a Python application. We use the Claude API for conversational logic because its instruction-following capability allows the AI to handle tangents and re-ask questions naturally. The entire conversational flow is defined in about 250 lines of code, providing clarity and control that visual builders lack.
The voice interface is built on AWS. We use AWS Polly for text-to-speech and AWS Transcribe for real-time speech-to-text. This combination provides latency under 400ms, creating a natural-feeling conversation. The system is connected to a phone number using the Twilio API to manage the call streaming. The entire application is deployed on AWS Lambda, keeping hosting costs under $60/month for firms handling up to 1,500 calls.
After each call ends, the complete transcript and a Claude-generated JSON summary are stored in a Supabase database. This summary extracts key fields like `injury_date`, `at_fault_party`, and `is_getting_treatment`. This structured data is then pushed to your firm's CRM, such as Clio or Filevine, via a direct API integration. A new contact and matter are created automatically in under 8 seconds after the call concludes.
For monitoring, the system uses `structlog` to create detailed logs for every call. If the AI fails to generate a valid case summary after 3 attempts, a Slack alert containing the full call transcript is sent to your intake team for immediate manual review. This failure rate is consistently below 2%, ensuring almost every lead is processed correctly without human intervention.
What Are the Key Benefits?
Answer Every Call, Instantly, 24/7
The AI answers on the first ring, any time of day. Capture lead details from a high-value case at 2 AM on a Saturday, ensuring you never lose a client to voicemail.
One Fixed Price, No Per-Minute Fees
We build and deploy your system for a single, fixed price. Your only ongoing costs are for direct AWS and Twilio usage, not an expensive per-seat or per-minute SaaS subscription.
You Own the Code and Intake Logic
You receive the full Python source code in your firm's GitHub account. Modify your intake questions as your practice evolves, without vendor lock-in or additional fees.
Instant Alerts When a Call Fails
Get an immediate Slack notification with the full call transcript for the 2% of calls the AI cannot process. This allows for rapid manual follow-up on complex or unclear cases.
Direct Integration with Your Legal CRM
We build direct API connections to Clio, Filevine, and PracticePanther. Qualified leads appear automatically as new matters, eliminating manual data entry for your team.
What Does the Process Look Like?
Intake Mapping (Week 1)
You provide your current PI lead qualification script and grant API access to your CRM. We deliver a conversational flow diagram for your review and approval.
Core System Build (Week 2)
We write the Python application, integrate the Claude and AWS voice services, and connect it to a Twilio phone number. You receive a private number for testing the AI's responses.
Integration and Deployment (Week 3)
We build the API connection to your CRM and deploy the complete system on AWS. You receive a test report showing successfully created contacts and matters in your CRM.
Go-Live and Handoff (Week 4)
We port your main intake number to the new system. After a two-week monitoring period, we deliver the final source code, documentation, and system runbook.
Frequently Asked Questions
- How much does a custom voice AI for lead intake cost?
- A typical personal injury intake system is a 3-4 week build. The final cost depends on the complexity of your intake script and the number of case types with different logic. A single-track motor vehicle accident screener is less complex than a system that branches for MVA, premises liability, and medical malpractice. We provide a fixed-price quote after a discovery call.
- What happens if a caller has a strong accent or is hard to understand?
- The system is designed to ask for clarification. If it is not confident it understood a key detail like an injury date, it will rephrase the question twice. If it still cannot capture the information, it notes the ambiguity in its summary and flags the call for human review. The full audio recording and transcript are always saved and available to your team.
- How is this different from using Smith.ai or a virtual receptionist service?
- Virtual receptionists are humans following a script. They are effective but have a high per-call cost, cannot scale instantly to handle call volume spikes, and introduce human error. Our system is software that can handle hundreds of calls simultaneously with perfect consistency. The usage cost is pennies per call, and it pushes structured data directly into your CRM without manual entry.
- How is confidential client information handled?
- The entire system is deployed in your firm's private AWS account, giving you full control over all data. Transcripts and summaries are stored in a secure Supabase database that you own, not on our servers. All data is encrypted in transit and at rest. This architecture provides significantly more security and control than using a multi-tenant SaaS application.
- Can we change the intake questions after the system is live?
- Yes. The qualification questions are stored in a straightforward configuration file within the Python code. You receive the complete source code and a runbook that explains how to edit this file. Any developer can update the questions in minutes. For clients on our flat monthly maintenance plan, we handle these changes within one business day.
- What happens if the AI or one of the APIs goes down?
- The system has multiple layers of redundancy. If the Claude API is down, the call is automatically routed to a standard voicemail that is transcribed and emailed to your intake team. The application runs on AWS Lambda, which is inherently fault-tolerant. We set up health checks that ping the system every 5 minutes and trigger an alert if there is an issue.
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