Demand models by segment, no-show predictor, upsell-probability scoring and thematic review analysis — trained on your PMS, your RMS and your real feedback, not on a sector benchmark.
Off-the-shelf RMSs optimise occupancy with a model that’s right on average. But your segment mix, real seasonality, channel behaviour and no-show patterns are specific — especially if you’re an independent hotel, a small chain or an apartment operator. A generic pricing recommendation leaves money on the table every night.
We build models trained on your real data: demand forecast by segment, per-booking no-show predictor, upsell-probability scoring by guest type and thematic review classification to close the loop with operations. Integrated with Mews, Cloudbeds, Opera and the most common RMSs without replacing anything.
Daily and weekly forecast by segment (corporate, leisure, groups, OTA) using your history, local events, school calendar and search data — feeds the RMS or the revenue team with your own forecast, not the vendor’s.
Model trained on your history: estimates no-show probability per booking combining channel, rate, guest type, lead time and country of origin — to tune overbooking without hurting the guest experience.
Predicts which guests are likely to take an upgrade, late check-out, transfer, F&B or experience, based on profile, travel reason and booking composition — so pre-stay only sends what has a real chance to convert.
Reads reviews from Booking, Tripadvisor, Google and Expedia, classifies them by theme (cleanliness, noise, F&B, reception, wifi, facilities) and sentiment — produces a monthly dashboard linking themes to shifts, departments and season.
Processes departure surveys, in-stay messages and reviews, extracts specific problem mentions (leak, noise, wifi down, repeated dish) and routes them as tickets to maintenance, housekeeping or F&B before they show up in a public review.
The RMS recommends prices with a generic model — the revenue team manually corrects every night. Overbooking is set with a flat 5% rule, leading to costly over-sell days. Reviews are read one by one and recurring themes are spotted late. Pre-stay upsell is sent the same way to everyone, with 4% conversion.
The in-house forecast cuts demand-prediction error to 8–10% and frees the revenue team from daily manual correction. The no-show predictor tunes overbooking per night and segment, eliminating critical over-sell. Upsell is sent only to guests with real probability to convert, lifting conversion to 11%. The review dashboard surfaces recurring themes (noise on the 4th floor) within a week, not a quarter.
Mid-market CRM with broad APIs — a natural fit for sales agents and lead enrichment.
Email, calendar and SharePoint as channel and context — triage, drafting and RAG over your inbox and files.
Seasonality is information, not an obstacle: models learn it if it’s well labelled. For hotels with limited history (recent opening, independent hotel without years of digital PMS) we combine your data with external signals — local events, school calendar, search trends, holidays — and start with simpler models that retrain as data grows. We don’t promise performance we can’t demonstrate.
We connect to Mews, Cloudbeds, Opera, SiteMinder and the most common RMSs via API or intermediate connector. The model reads from the PMS and writes its predictions back into booking properties or a revenue-team dashboard — it doesn’t replace the RMS, it feeds or complements it. The rule is: no forced migrations to get started.
You do. We train in your cloud tenant or in private infrastructure of your choice, the model weights are yours and the data isn’t used to train anything shared across clients. If you ever want to take the model to another provider or bring it in-house, it’s documented and portable.
We start with transfer learning from a similar hotel in your portfolio and retrain as new history grows. For a new segment (say MICE groups in a previously leisure-only hotel) we revisit features and, if needed, train an additional dedicated model. It’s part of the service model, not a new project every time.
We design for privacy from the start, human control, traceability, usage limits, permissioning and documentation. For sensitive processes, we help assess risk and applicable obligations under GDPR and the EU AI Act.
Every engagement is led personally by one of the partners. If there's a fit, you get a personal first read of your case within one business day — not a canned demo.
