Workflow and actors
The PMS feeds demand forecasting, no-show risk and upsell propensity; the revenue team uses those signals in RMS and planning, while reception and operations receive incidents extracted from feedback.
Service · Custom AI · Hotels & hospitality
Custom AI for hotels that learns from every stay.
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.
The workflows, examples and figures on this page are illustrative composites and modelled targets, not measured client results. In a real project, we define the baseline, thresholds and human review with your data before rollout.
What we solve
Your hotel isn’t an average RevPAR case.
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.
Implementation contract
How it runs in production
Systems and data
Bookings, rates, channel, lead time, stay and feedback data from Mews, Cloudbeds or Opera combine with the RMS, calendar, events and reviews, respecting the purpose and retention of each dataset.
Exceptions and risks
Hotels or segments with limited history, unusual events, channel shifts, seasonal drift, low-confidence bookings and ambiguous feedback are separated for review or a dedicated model.
Human review
The revenue lead approves recommendations outside the confidence threshold or overbooking limit; reception validates sensitive changes and each department confirms incidents before closing them.
Implementation pattern
Versioned models by hotel and segment, scheduled training, daily or per-booking inference and monitoring against actual closes. Outputs carry confidence, explanation and an approval record.
Relevant integration
Mews, Cloudbeds or Opera with the RMS and SiteMinder, plus the revenue dashboard and maintenance, housekeeping or F&B task system through supported APIs.
What we build for this sector
Use cases that ship to production.
Forecast
Demand model by segment
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.
Modelled target: MAPE −30% vs RMS forecast, by hotel
Predictive
Per-booking no-show predictor
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.
Modelled target: Recall > 0.7 on no-shows, by mix
Revenue
Upsell-probability scoring
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.
Modelled target: +20–25% upsell conversion vs generic blast
Reputation
Thematic review classifier
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.
Modelled target: 10+ standardised themes
Operations
Problem-mention extractor from feedback
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.
Modelled target: Ticket created in < 5 min
Illustrative composite scenario · modelled figures and targets, not client results
An urban 180-room hotel.
Four-star urban hotel with a 60/40 corporate–leisure mix and two years of history in Mews and a standard RMS. Illustrative composite scenario; the figures below are a modelled baseline and targets, not measured client results.
Modelled baseline
Baseline assumption: 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.
Modelled target
Modelled operating target: 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.
Modelled target: +6.5% RevPAR attributable to in-house forecast and upsell
We connect to your stack
Integrations that matter in this sector
Frequently asked
What clients ask us
- 01
We have strong seasonality and not many years of data. Will it still work?
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.
- 02
How does it integrate with our PMS and RMS without breaking what works?
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 hotel sets the confidence threshold and overbooking limit; any recommendation outside them stays pending until the designated revenue lead approves it, and the decision is logged. The rule is: no forced migrations to get started.
- 03
Who owns the model and the training data?
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.
- 04
What happens if we open a new hotel or launch a new segment?
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.
Same sector, other services
Trust
Safe, traceable AI,
enterprise-ready.
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.
- 01We never train models on your data without explicit authorization.
- 02Human review built-in for processes where risk demands it.
- 03Traceability: prompts, sources, permissions, errors and metrics, all documented.
- 04Privacy, security and control integrated from day one.
- 05Solutions engineered to be maintained, audited and improved over time.
GDPREU AI ActAEPDISO 27001 readyEU data residency
Personal diagnosis
We work with
few clients.
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.
How we work
- 01Tell us which process eats your time
- 02Personal reply within one business day
- 0320-minute call, no demo, no pitch