Workflow and actors
On a schedule or event, the workflow collects bookings, rates, allotments, reviews and KPIs, normalises them by property and room type, reconciles differences and delivers a report or alert to revenue, management or reception.
Service · AI automation · Hotels & hospitality
AI automation for hotels that runs behind reception.
Multichannel review aggregation, PMS/RMS/channel reconciliation, revenue reports, no-show and overbooking alerts and overnight KPI ETL, unattended workflows on top of your existing stack.
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
Mews says one number, the RMS says another, the channel manager a third.
Bookings come in from Booking, Expedia, your own site and agencies. Rates live in the RMS. Inventory in the PMS. Reviews scattered across Booking, Tripadvisor and Google. Every Monday someone spends half a shift squaring the numbers before the revenue director can decide anything, and when there’s an overbooking, they find out from a guest standing at the front desk.
We design automations that run overnight or as soon as a data point changes: they read Mews, Cloudbeds or Opera, cross-check with the RMS and channels, catch the mismatch, aggregate the reviews into a single report and send the no-show or overbooking alert to the internal channel the moment it appears. No need to migrate any of the stack that already works.
Implementation contract
How it runs in production
Systems and data
It works across the PMS, RMS, channel manager, review sources and, where included, F&B and housekeeping data, using consistent property, room, booking, date and currency identifiers.
Exceptions and risks
Delayed feeds, duplicate bookings, time zones, incomplete room mappings and uncertain no-show signals are flagged, not turned into automatic rate, allotment or booking changes.
Human review
Revenue validates mismatches and inventory or pricing decisions; reception handles overbookings and no-shows; management reviews summaries and reputation themes before acting with guests or teams.
Implementation pattern
We use scheduled or webhook-driven idempotent ingestion, a canonical model per property and deterministic reconciliation rules. AI classifies reviews and drafts summaries only after the data checks pass.
Relevant integration
Available APIs and webhooks for Mews, Cloudbeds, Opera, the RMS, channel manager, Microsoft 365 or Teams determine frequency and actions; scopes are validated before each flow is enabled.
What we build for this sector
Use cases that ship to production.
Reputation
Multichannel review aggregation and analysis
Reads new reviews on Booking, Tripadvisor, Google and other channels, classifies them by topic (cleanliness, F&B, reception, noise, wifi) and sentiment, and produces the weekly report for management with recurring themes and affected rooms.
Modelled target: 100% reviews analysed within 24h
Revenue
PMS, RMS and channel reconciliation
Cross-references bookings, rates and inventory between Mews or Cloudbeds, the RMS and the channel manager, detects parity or allotment discrepancies and opens the ticket with context before it becomes an overbooking.
Modelled target: Mismatches detected in < 15 min
Reporting
Automated revenue management reporting
Generates the daily, weekly and monthly occupancy, ADR, RevPAR and pickup reports from the PMS and RMS, drafts the executive summary in plain language and drops the report into Microsoft 365 ready for the committee.
Modelled target: 1 day/week back per revenue manager
Operations
No-show and overbooking alerts
Watches pickup, guarantees and expected occupancy by room type, anticipates no-shows using guest history and pushes the overbooking alert to the team via WhatsApp Business or Microsoft Teams as soon as risk appears.
Modelled target: −72% surprise overbookings
Data
Overnight operational KPI ETL
Each night extracts data from PMS, RMS, channel manager, F&B and housekeeping, unifies it into your data warehouse or BI layer and leaves the operational KPIs ready for the first coffee of the morning.
Modelled target: Unified stack without touching the PMS
Illustrative composite scenario · modelled figures and targets, not client results
A chain of 8 hotels, 950 rooms.
Urban hotel chain on Mews with a standard RMS and channel manager. Central revenue team of three. Illustrative composite scenario; the figures below are a modelled baseline and targets, not measured client results.
Modelled baseline
Baseline assumption: Mondays and Thursdays spent squaring numbers between PMS, RMS and channels before the revenue committee. Reviews read by hand by property managers when they have time, with no aggregation across hotels. Overbookings discovered at reception with the guest already there. ETL into a master Excel maintained by a single person.
Modelled target
Modelled operating target: PMS/RMS/channel reconciliation at 06:00 every morning, with mismatches notified to the revenue manager via Microsoft Teams. Weekly review report aggregated across the 8 hotels, with recurring themes tagged. Overbooking alerts 12-36h ahead of check-in. KPIs unified in BI without touching Mews.
Modelled target: +11% RevPAR attributed to faster decisions
We connect to your stack
Integrations that matter in this sector
Frequently asked
What clients ask us
- 01
Are you going to break Mews, Cloudbeds or Opera?
No. We work on top of the public APIs of Mews, Cloudbeds, Opera, SiteMinder and the main RMS systems. The automation reads and, when it needs to write, does so through supported channels, we don’t patch the PMS or install anything inside it. If something changes in the PMS due to an update, we absorb it in the middle layer.
- 02
What about GDPR and guest data?
Deployment lives in your infrastructure or a European tenant. Guest personal data is anonymised in operational logs and only used for the workflow in question, no models trained on it. You have traceability of which automation accessed which data and can export the full trail for any audit.
- 03
How long until the first workflow is in production?
The first useful workflow, usually PMS/channel reconciliation or review aggregation, is in production in 4-6 weeks, on your real data and with before/after metrics measured. Then we scale the rest of the catalogue based on return.
- 04
What needs human attention and what runs unattended?
Mismatches within tolerance, neutral or positive reviews and the overnight ETL all run unattended. Overbookings, reviews with explicit complaints or sensitive topics, and mismatches above threshold trigger an alert to the internal channel you define, with context prepared so the revenue manager or director decides in seconds.
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