Question 1

How do you keep AI services out of PCI-DSS scope when they touch the hotel POS or front desk?

Accepted Answer

Tokenise PANs at the perimeter — typically the payment gateway or a P2PE-validated terminal — and have everything downstream (the recommender, the concierge agent, the upsell ranker, the loyalty service) operate on tokens only. The cardholder data environment (CDE) shrinks to the gateway and a small reconciliation enclave. AI inference, training data, feature stores and logs sit outside the CDE and never receive PAN, CVV or magstripe / EMV track data. We document the scope in a written SAQ-D or ROC narrative for the QSA before go-live.

Question 2

How do you build OpenTravel / HTNG-compliant integrations into Opera Cloud and other PMS systems?

Accepted Answer

Reads use OPERA Cloud Distribution API (OCDA) for availability, rates, and inventory, plus the OPERA Cloud REST APIs for guest profile, reservation, folio and stay data. Writes are restricted and queued — reservation create / modify / cancel go through HTNG 2008B Reservation messages or the equivalent OPERA REST endpoints, with idempotency keys and reconciliation against the daily revenue audit. For multi-PMS estates we normalise to an internal OpenTravel-aligned schema so Mews, Cloudbeds and Maestro feeds land in the same shape as Opera.

Question 3

How do you run dynamic pricing without triggering OTA rate-parity violations on Booking.com or Expedia?

Accepted Answer

Two layers. First, a rate-parity monitor scrapes the public OTA rate for each shopped room-type / arrival-date / length-of-stay combination every few hours and flags drift against the channel-manager push (SiteMinder, RateGain). Second, pricing actions are constrained: we model parity as a hard rule per channel contract (best-rate-guarantee, MFN, narrow-parity, wide-parity, no-parity) and the optimiser only proposes rates that satisfy the most restrictive active contract. Override rates for loyalty members, package rates and closed-user-group rates are tagged so they fall outside parity.

Question 4

How do you build a conversational concierge agent that does not hallucinate room availability or restaurant times?

Accepted Answer

The agent never answers availability, rate, or service-hours questions from the language model directly. Every such intent triggers a tool call: room availability hits the PMS / CRS in real time, restaurant and spa hours come from a curated property catalogue with timestamped versioning, and bookable inventory goes through the same OPERA / Mews booking endpoint a human agent would use. The LLM is restricted to NL understanding, NL generation and grounded summarisation. We also ship a refusal policy for any out-of-catalogue claim and red-team the agent against hallucinated availability before launch.

Question 5

How do you satisfy GDPR right-to-erasure across PMS, CRM, loyalty and review platforms?

Accepted Answer

A central erasure orchestrator fans out the request: PMS guest-profile soft-delete and tokenisation (Opera, Mews, Cloudbeds), CRM record deletion or pseudonymisation (Salesforce Hospitality Cloud), loyalty record anonymisation while preserving aggregate balance reporting, review-platform takedown requests via the API where the property posted on behalf of the guest, and search-index / vector-store purges so the concierge agent can no longer retrieve the guest. We retain only what statute requires (e.g. fiscal records under local hotel-tax law) and document the legal basis per field, per system.

Question 6

How do you build demand forecasting that actually uses weather, events and competitor signals?

Accepted Answer

The base model is a hierarchical forecast at room-type / arrival-date / length-of-stay grain using historical pickup curves. On top we add exogenous regressors: weather (multi-day temperature, precipitation and severe-weather flags from a paid weather API), local events (concerts, conferences, sports fixtures, school holidays — we maintain a curated event calendar per market), competitor rates (shopped from the public web on a controlled cadence), flight-search and flight-load signals where available, and macro indicators (FX rate for inbound markets, fuel cost). The lift over a naive pickup curve is usually largest on event weeks and shoulder season — the two windows where revenue teams most need it.

Question 7

How do you close the loop from review sentiment back into operational action?

Accepted Answer

Reviews from TripAdvisor, Google, Booking.com and on-property survey tools (Medallia, Qualtrics, GuestVoice) flow into one normalised store. An aspect-based sentiment model tags each review at the (department, sub-aspect, sentiment) level — e.g. (Housekeeping, bathroom-cleanliness, negative). Trends per property per week feed a triage queue routed to the right HOD with a suggested action template. We track time-to-action and the downstream effect on rolling NPS / GuestVoice score, not just review volume — closing the loop is the point.

Question 8

How do you optimise energy / HVAC against occupancy without leaking guest privacy?

Accepted Answer

Occupancy signals come from the PMS room-status feed (vacant-clean, vacant-dirty, occupied, do-not-disturb) and from in-room presence sensors that emit only a boolean and a coarse activity flag — never identity, never images, never audio. The HVAC controller runs an MPC loop against thermal-comfort bands, weather forecast, electricity tariff and the occupancy signal. Guest-identifying data never enters the HVAC plane. Energy savings on a typical mid-scale property run in the high single digits to low double digits versus a fixed-setpoint baseline, with no reduction in measured guest-comfort scores.

Hotel-grade AI: Opera / Mews integration, RevPAR-lift pricing, PCI-DSS-safe concierge agents

What we build to

Hospitality AI Systems

Dynamic Pricing & Revenue Management

OTA Rate-Parity Detection

Conversational Concierge Agent

Demand Forecasting with Exogenous Signals

Personalised Upsell at Booking & Check-in

Voice & Kiosk Check-in with PMS Integration

Hospitality Data Engineering

PMS + Channel + GDS Unification

Loyalty + CRM + Folio Stitching

Review & Sentiment Pipeline

Occupancy-Aware Energy / HVAC Plane

Frequently Asked Questions