AI in hospitality with a focus on revenue management.

The big names in revenue management, IDeaS, Duetto, Atomize, OTA Insight and Cendyn, are excellent if their model suits you. For enterprise hotel chains such as Accor, IHG or Marriott running a locked-down Opera or Mews stack, that is often an excellent starting point. But we are called by the very parties for whom it doesn't fit.

A mid-market hotel chain that cannot afford IDeaS licence fees but still needs a measurable revenue impact. A holiday park where weekend, weather and local events carry more signal than a standard package can handle. A restaurant chain for which no hotel RMS exists. A boutique hotel with its own pricing philosophy that does not want a vendor overriding it with a black-box algorithm. A conference or MICE venue where room planning, group bookings and F&B margins all need to sit within the same optimisation.

We build a dedicated layer on top of your existing PMS for this. Demand forecasting that takes your local signals into account, length-of-stay optimisation that suits your room allocation, and channel mix models that favour direct bookings over expensive OTA commissions, all fed by your own historical data rather than a black-box benchmark you can never be sure is representative of your market. We integrate that layer with a real-time analytics platform so your revenue team does not have to wait for tomorrow's report.

The difference lies in philosophy. A vendor such as IDeaS sells to a thousand hotels and must therefore generalise its model. We build for a single client and can calibrate model parameters, data points and decision rules precisely to how your market works, which segments you serve and which distribution channels you want to strengthen. For the clients where it fits, that delivers measurably better ADR and RevPAR than an off-the-shelf package.

Revenue management is the most valuable use case, which is why it sits at the core of what we build for hospitality clients, but it is rarely the only place where AI makes a difference. A revenue project usually sits alongside three to five further models that together complete the picture.

Demand forecasting by room category, channel and length of stay, not only for pricing but also for staff planning, housekeeping and F&B purchasing. Customer segmentation through RFM and value models that focus your marketing budget on guests with the highest lifetime value. Personalised marketing via Revinate or Cendyn, with AI-generated email campaigns and offers that match previous stays and recorded preferences.

Chatbots for 24/7 multilingual guest enquiries on your website and WhatsApp, powered by your own FAQ and booking data. Voice assistants in rooms via Alexa for Hospitality or similar integrations for lighting, climate, room service and local tips. Predictive maintenance on HVAC, lift and pool installations, so that faults are prevented rather than a guest calling reception in the middle of the night.

For restaurants and F&B chains we also build waste prediction (how much fresh produce you will need tomorrow, based on weather, day and events), menu optimisation (which dishes deliver both margin and satisfaction) and no-show models that determine how much overbooking is responsible. On the guest side: sentiment analysis on TripAdvisor and Booking reviews that surfaces patterns per location, and complaint classification that automatically routes incoming emails or chat messages to the right department. A housekeeping remark goes to housekeeping, a pricing complaint to the revenue manager, and a safety report to management.

Which models take priority depends on where your operation loses the most time or margin. We map that out in the audit phase. A project often starts with dynamic pricing and demand forecasting, and grows organically with the models that deliver the most value in phase two.