Automate Client Calls and Scheduling with an AI Voice Agent

Standard business phone systems use rigid IVR menus ("Press 1 for sales..."). Callers hate them and often hang up. A potential client with a specific question about shingle types will not navigate a five-layer menu; they will call the next contractor on Google.

A homeowner calls about a leaky roof. They want to know if you service their town, if you offer emergency tarping, and what the initial inspection fee is. A simple chatbot builder or a basic call forwarding service cannot answer these multi-part questions. It can only route calls or collect a name and number. The lead either gets frustrated or leaves a message, and by the time you call back 2 hours later, they have already booked with a competitor who answered the phone.

Off-the-shelf voice AI solutions sound impressive but lack the specific context of your business. They cannot check your team's real-time availability in Acuity Scheduling or query your price list in a Google Sheet for "asphalt shingle repair per square foot." This forces them to give generic answers or escalate every call to a human, defeating the purpose of automation. Your business logic is unique; a generic tool cannot capture it.

Syntora's approach for an AI voice agent begins with a discovery phase to document your most common client questions and lead qualification criteria. These would be mapped to a structured knowledge base stored in Supabase, utilizing pgvector for semantic search. This setup allows the agent to find precise answers, rather than relying solely on keyword matches. Syntora would integrate with your existing calendar API, such as Calendly or Acuity, to access real-time availability.

The core agent architecture typically involves a FastAPI application deployed on AWS Lambda. When an incoming call connects via a Twilio phone number, Lambda invokes the Python function. The audio is transcribed, and the text is then sent to the Claude 3 Sonnet API alongside your knowledge base. Claude generates a conversational response, which is converted to speech and streamed back to the caller. Syntora has built document processing pipelines using Claude API for financial documents, and similar patterns apply to voice agent interactions for construction-related inquiries.

For appointment scheduling, the agent would follow a defined workflow. It would confirm the service type, verify the caller's address against your service area using a PostGIS query in Supabase, and present available slots from your calendar. Upon confirmation, the agent would book the appointment via your scheduling tool's API and send a confirmation SMS using Twilio.

All call transcripts and agent actions would be logged to a Supabase table. Syntora would build a Vercel dashboard for reviewing conversations and agent performance. An alert mechanism, such as a Slack notification triggered by a structlog entry, could be configured for instances where the agent is unable to answer a question after multiple attempts. The delivered system would include the full codebase and documentation.