Automate Your Dispatch with a Custom Voice AI Agent

Many field service businesses rely on pre-built Interactive Voice Response (IVR) systems from providers like Twilio. These are rigid, menu-driven systems. They work for simple routing but fail with natural language. A customer saying “My AC unit is making a grinding noise, can you send someone tomorrow morning?” breaks a flow designed for “Press 2 for service.” You end up building a complex tree that still dumps most callers to a human.

Off-the-shelf voicebots are the next step, but they are typically designed for simple FAQs, not complex scheduling that requires real-time checks against a technician's calendar. They fail when they need to query a third-party CRM with specific business logic, like confirming if a technician is certified for a particular appliance model. This lack of deep integration turns the voicebot into a simple message-taker.

The only other option is hiring more dispatchers, but that scales cost linearly with call volume and introduces training overhead. A new dispatcher can take 2-3 months to learn your technician specialties and service areas, leading to booking errors that cost you truck rolls and customer trust. The core problem is that generic tools cannot handle your specific operational logic.

Syntora's approach to building a voice AI dispatch system begins with a thorough discovery phase. We would start by analyzing 20-30 of your existing call recordings. These recordings would be transcribed and then processed using Anthropic's Claude API to identify core customer intents, such as scheduling a new job, checking an existing appointment, or requesting a quote. This detailed intent mapping forms the foundation for the AI agent's conversational design and prompt engineering. We have experience building similar document processing pipelines using Claude API for financial documents, and the same pattern applies effectively to conversational data analysis.

The core logic for the AI agent would be built as a Python service using the FastAPI framework. This service manages the conversation state and uses the Claude API for intent recognition and extracting key entities like addresses, service types, and requested times. For real-time availability checks, the system would use `httpx` to make non-blocking API calls to your existing scheduling system or CRM, ensuring the AI agent only offers genuinely open appointment slots.

For deployment, the entire service would be designed for a serverless architecture, typically on AWS Lambda. This setup allows the system to scale efficiently from handling a few calls to managing many concurrent conversations without manual intervention, keeping operational costs predictable. Conversation state and booking details would be stored in a Supabase Postgres database. This enables the agent to maintain full context across interactions, allowing for smooth call resumptions or follow-up questions.

Integration with a telephony provider like Twilio would manage the voice stream. Audio from incoming calls would be transcribed, processed by the FastAPI service, and the AI's generated response would be converted back to speech. A well-engineered system aims for minimal latency in this round-trip process, typically achieving response times that contribute to a fluid and natural conversational experience.