Get a Custom-Built AI Voice Agent for Your Hotel

Most hotels rely on their phone system's basic Interactive Voice Response (IVR). These systems route calls with simple menus like 'Press 1 for reservations,' but they cannot resolve a single guest issue. They push work onto already busy front desk staff. Some properties try off-the-shelf voice bots from platforms like Twilio, but these are generic toolkits. They lack the built-in logic for hospitality and cannot access real-time availability in a PMS like Mews without fragile, expensive custom development that hotel staff cannot maintain.

Consider a guest calling at 10 PM. They want to confirm their reservation for tomorrow, ask if their room has a bathtub, and inquire about airport shuttle times while the front desk is busy with a late check-in. The basic IVR can only send them to a full voicemail box. A generic bot might answer the shuttle question by scraping the website, but it cannot access the booking in Oracle Opera to confirm reservation details or check room-specific features. The guest gets frustrated, hangs up, and might leave a negative review before ever arriving.

The structural problem is the data gap. Off-the-shelf voice platforms are completely disconnected from the hotel's central nervous system: the Property Management System (PMS). They treat guest interaction as a separate function from property operations. A real solution requires deep, real-time, two-way integration. The voice agent must be able to not just read but also write data back to the PMS, for example, adding a 'late check-in' note to a reservation. Generic platforms are architected for one-way information retrieval, not two-way operational control.

The project would begin with an audit of your hotel's current call patterns and technology stack. Syntora would analyze call logs to identify the 5-10 most common guest inquiries that can be automated. We would also map the API capabilities of your specific PMS, whether it's a modern platform like Cloudbeds or a legacy system like Oracle Opera, to determine what data is accessible for real-time lookups and updates.

The AI voice agent would be built as a Python application using FastAPI to handle incoming calls via a webhook. For understanding guest speech, we would use a state-of-the-art speech-to-text model, and the Claude API would parse the transcribed text to identify one of over 20 intents and extract entities like dates and names. This approach allows for nuanced, conversational interactions. The system would run on AWS Lambda for cost-effective, high-availability call processing at under 500ms per turn, can handle up to 100 concurrent calls, and typically costs under $50/month to operate.

The final deliverable is a fully managed voice agent integrated with your phone number and PMS. When a guest calls, the agent answers instantly, 24/7. You receive a runbook, full source code in your own GitHub repository, and a Supabase dashboard to view call logs, transcripts, and common unresolved queries. This dashboard provides visibility and helps identify areas for future improvements to the agent's logic.