Guest Insight — The AI-Powered Digital File
Every guest has a digital file sitting in the PMS — never fully read, never synthesised, never acted on. Lucy changes that. By pulling the PMS digital file and combining it with everything Lucy already knows about the guest, she generates a rich visual staff intelligence briefing before every arrival. No other hospitality AI does this.
The Problem Hotels Have Right Now
The PMS Digital File
Exists. Rarely read. Guest preferences, past complaints, loyalty data, staff notes from 2019 — all sitting in Opera, untouched before arrival.
The Staff Reality
Front desk staff greet guests with no context. "Good evening, welcome back" — they have no idea it's the guest's 4th stay, that they had a noise complaint, or that they book the spa every time.
The Missed Opportunity
Every returning guest is treated like a first-time guest. Loyalty erodes. Upsells are missed. Complaints repeat. The data was always there.
What Lucy Pulls from Opera (OHIP)
Guest Identity
/crm/v1/profilesName, nationality, language preference, loyalty tier, VIP flags, contact details
Guest Preferences
/crm/v1/profiles/{id}/preferencesPillow type, floor preference, dietary requirements, room temperature, amenity preferences
Staff Notes & Alerts
/rsv/v1/reservations/{id}/reservationNotes"Champagne on arrival", "noise complaint Mar 2025", "feather allergy", custom alerts from all previous stays
Full Stay History
/crm/v1/profiles/{id}/stayStatisticsAll past stays: dates, room types, length, properties visited, total lifetime spend
Current Reservation
/rsv/v1/reservationsRoom number, check-in/out dates, rate code, package inclusions, accompanying guests
Folio & Spend
/fof/v1/foliosRoom charges, F&B spend, spa charges, extras — real-time spend profile for this stay
What Lucy Already Knows Internally
ConversationMemory
Everything they've said to Lucy across all stays — topics, vendors they liked, things to avoid, memorable quotes
UserProfile
Inferred Big Five personality traits, communication style (formal/casual), verbosity preference, decision style, estimated age group
VendorBooking
Every restaurant, spa, activity they've actually booked through Lucy — with dates, parties, special requests
LucyFeedback
What they rated, helpfulness scores, complaints about Lucy's responses, areas where service fell short
GuestPredictiveProfile
ML-inferred predicted needs: likely to book spa, prefers quiet floors, responds to upsells, travel style classification
ExternalServiceBooking
Flights, car hire, rail bookings arranged via Lucy — builds a picture of their full travel pattern
The Synthesis — How Lucy Generates the Insight Profile
Example Output — What Staff See
Personality Snapshot
— Lucy Generated""Mr Chen is an introverted, high-autonomy luxury traveller. Analytical and detail-oriented. Prefers minimal staff interaction — appreciates efficiency over warmth. Responds well to precision and pre-empting needs rather than asking. Tech-comfortable, likely to self-serve via Lucy rather than call front desk.""
Predicted Preferences (This Stay)
— Lucy Generated""Based on 3 previous stays and Lucy conversation history: Very likely to book spa (booked on 3 of 3 stays — consider pre-offer at check-in). Likely to order vegetarian room service. Prefers room temperature 18°C. Will probably request early departure — book transport pre-emptively. Responds to Michelin restaurant recommendations.""
Risk Flags
— Lucy Generated""⚠ Left noise complaint March 2025 (room adjacent to elevator). Assign quiet floor, minimum room 420+. ⚠ Feather allergy noted in PMS — verify hypoallergenic bedding confirmed. ⚠ Complained about slow room service response in Lucy chat (Oct 2025) — flag to F&B team.""
Upsell Opportunities
— Lucy Generated""High probability upsells: Spa half-day (booked every stay — worth pre-booking at check-in before slots fill). Butler upgrade (high spend profile, values service efficiency). Michelin dinner recommendation (asked Lucy for restaurant ideas twice last visit — proactively offer tonight's availability at The Grill).""
Suggested Staff Opening
— Lucy Generated""Welcome back, Mr Chen. We've prepared your room on the upper floors with hypoallergenic bedding as noted in your profile. I've also taken the liberty of enquiring about availability at The Grill for this evening — shall I confirm a table for one at 7:30?""
Multi-Hotel PMS Adapter — One Lucy, Every PMS
Since Lucy is a platform for multiple hotel groups — not just Café Royal — the Guest Insight engine uses a PMS Adapter pattern. Each hotel's configuration declares its PMS type; Lucy routes all data calls through the correct adapter automatically.
// Hotel configuration (per property)
{
"pms_type": "opera_cloud" | "mews" | "apaleo" | "cloudbeds",
"pms_api_key": "...",
"pms_property_id": "...",
"helvar_router_ip": "192.168.1.50", // if Helvar installed
"helvar_room_group_map": {"101": 1, "102": 2, ...}
}
Opera Cloud (OHIP)
REST API·OAuth 2.0 — OHIP Partner PortalFull guest profiles, preferences, notes, stay history, folios
Opera 5 (Legacy)
SOAP/XML·Integration Partner Program (approval required)Same data, older protocol — XML parsing layer required
Mews
REST API·OAuth 2.0 — open developer portalCustomers, reservations, billing, tasks, accounting
Apaleo
REST API·OAuth 2.0 — self-serviceModern cloud-native — favoured by boutique/lifestyle hotels
Cloudbeds
REST API·API keyGuests, reservations, rooms, maintenance, charges
Privacy & GDPR Compliance
Guest Insight is a staff-only feature — never guest-facing. Data pulled from PMS is processed in-memory for the briefing and not stored beyond the session. Lucy's existing GDPR framework (GDPRDataRequest, GDPRDataDeletion entities, consent auditing) governs all data handling. Hotels must declare Guest Insight data processing in their guest privacy notices.
Every guest has a story. Lucy reads it before they arrive.
Guest Insight transforms the PMS from a booking ledger into an intelligence engine. For the first time, every staff member walks into a guest interaction fully briefed — not just on their booking, but on their personality, preferences, history, and what they're likely to want before they ask.