Automate Guest Check-In with Custom AI Engineering

Many hotels use modern PMS platforms like Cloudbeds or Mews, which offer some basic automation. They can send a templated welcome email 24 hours before arrival, but they cannot manage a real conversation. If a guest replies asking for an early check-in and directions from the airport, the PMS automation fails. The system is designed for one-way notifications, not two-way, context-aware dialogue.

To fill this gap, properties add messaging tools like Whistle or Kipsu. These platforms use simple keyword triggers. If a guest texts, "Can I check in around 2 PM and do you have a crib?", the system might only see the words "check in" and send a generic response about standard check-in times, completely ignoring the crib request. This forces front desk staff to constantly monitor the inbox for failed automations and intervene manually.

Consider a 30-room boutique hotel during a wedding block arrival. Ten parties arrive in one hour, all texting questions, sending ID photos, and asking about parking. The front desk agent must juggle these conversations while manually looking up each reservation in Mews, visually matching the ID photo to the guest name, checking room status, and then typing out responses. Each multi-step check-in takes over 5 minutes of fragmented work, creating queues and a stressful guest experience.

The structural problem is that off-the-shelf tools treat guest communication and PMS operations as separate domains. The guest's message is just text to the communication platform, and the check-in is a series of clicks in the PMS. There is no software bridge that can understand the guest's intent from unstructured language and translate it into a structured transaction within the PMS. This requires custom engineering.

The engagement would begin with a thorough audit of your current check-in process and technology. Syntora maps every guest interaction, from the initial booking confirmation to the digital key delivery. We would review your PMS API documentation, like the Mews Connector API, to confirm its capabilities for real-time updates. This audit produces a technical blueprint detailing how the AI agent will securely connect to your existing systems.

The core of the system would be an SMS and voice agent built in Python using the Claude API for natural language understanding, deployed on AWS Lambda for efficient, event-driven computing. When a guest texts, the Claude API interprets their intent and extracts key data. A secure FastAPI service then uses this data to query your PMS, verify room status, and confirm guest details. For ID verification, we can integrate a third-party service or build a custom OCR pipeline to extract data from a driver's license photo and match it against the reservation.

The delivered system would autonomously handle up to 80% of routine check-in conversations. A guest could text a photo of their ID and receive their room number and a digital key code in under 60 seconds. A full transcript of the interaction is automatically logged in the guest's profile within your PMS. You receive the complete source code, a deployment runbook, and a dashboard to monitor performance.