Automate Patient Calls with a Custom AI Agent

Most dental offices rely on standard voicemail or an Interactive Voice Response (IVR) system. These frustrate patients with rigid menus and long waits. When a potential new patient calls with a specific question like, “Do you accept Delta Dental PPO for cosmetic work?”, a simple IVR cannot answer. The caller hangs up and tries the next clinic on their list.

Practice management add-ons from providers like Weave or NexHealth offer more advanced features, but they are often generic. They can answer, “What are your hours?” but fail on complex queries like, “I have a toothache and my insurance is Cigna, can I get an emergency appointment today?” The system can't check real-time insurance eligibility against the live schedule, so it defaults to, “Please call our office during business hours,” which loses the lead.

This scenario costs offices real money. A patient calling at 7 PM about an emergency root canal is a high-value lead worth over $2,000. If your system takes a message, your staff might not call back until 9:30 AM the next day. By then, the patient has already found and booked with another practice that provided an immediate answer.

Syntora's approach to developing an AI agent for patient calls begins with a comprehensive data ingestion phase. We would start by auditing and consolidating your practice's information into a structured knowledge base. This includes processing website content, service price lists, accepted insurance documents, and staff bios. This data would be stored in a Supabase Postgres database, creating a single source of truth the AI agent can query using the Claude API. We have built document processing pipelines using Claude API for financial documents, and the same pattern applies to dental practice documents.

The system's architecture would involve configuring a new phone number through Twilio, which forwards calls to your existing line. When a patient calls, an AWS Lambda function executes a Python script. It uses a real-time speech-to-text service to transcribe the caller's words. This transcript is then sent to the Claude API, which queries the Supabase knowledge base to find relevant information and generate a natural language response.

The AI-generated answer is converted back to speech and played for the patient. For appointment requests, the agent would check your live schedule via direct API integration with your practice management software or a shared Google Calendar. The agent would confirm available slots and could book the appointment directly. The patient would immediately receive an SMS confirmation.

Throughout the process, every call transcript and AI response would be logged for quality assurance. The system would include a mechanism to route calls to your front desk if the AI agent's confidence in its answer is low, or if the patient explicitly requests to speak with a human. This ensures complex cases are handled by staff. The typical build timeline for a system of this complexity ranges from 4 to 8 weeks, depending on the client's data readiness and integration requirements. The client would need to provide access to their practice's documents, calendar, and any relevant APIs. Deliverables would include the deployed AI agent system, a structured knowledge base, and documentation for operation and maintenance. Operational costs for cloud services typically scale with call volume and can be estimated during the architecture phase.