Automated hotel reservations with AI

Challenge

A boutique hotel group faced high operational load in central reservations. The team relied on manual email and phone handling, manual payment and guarantee checks, and manual contract management. More than one third of bookings arrived by phone or email, and the organization lacked centralized dashboards and live performance tracking.

Processing reservations needed to scale without linear headcount growth. Key problems included slow, manual email categorization and extraction, fragmented tool integrations across Outlook, PMS and archive storage, and manual payment validation that increased error risk.

Solution

We designed and delivered an AI-first, web-based hub as an MVP running on LTPlabs infrastructure. The web application classifies and extracts live email content, lets users read and edit reservation details and reply from the hub,and mirrors actions done in Outlook to preserve inter-tool workflows.

Key technical features included LLM based automatic categorization, LLM based extraction of reservation fields, and LLM based template email generation. The solution also provided automatic archiving to OneDrive, reminders for payment checks, and simple operators to speed PMS updates. The platform was built with a scalable, modular architecture and business facing analytics.

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Results

The pilotinvolved two key users and one reference hotel and produced measurableoperational outcomes. Reported metrics from the pilot are:

• Before, e-mail categorization took around two hours every day. Now, the web application is continuously updated with the latest e-mails, with automatic categorization taking less than 2 minutes.
• Within the new hub, e-mail extraction takes five seconds per email and is optimized to only happen with specific triggers, minimizing cost.
• Platform activity and throughput during the 2-week pilot included 200 plus runs, 1800 plus emails extracted, 35 plus auto filled emails sent, and 600 plus emails archived.

Qualitative outcomes included reduced time spent on categorization and a set of process and product improvements driven by structured pilot feedback.

This AI reservations’ hub removed repetitive, low value tasks from the reservations workflow by automating classification, extraction, template reply generation, and archiving. The pilot demonstrated the hub can process live email traffic and create a foundation for deeper automation, including planned PMS integrations and broader rollout across hotels.