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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
