The Zero-Friction Resort OS

Channel sync — resilient by design:

• Automatically detects when a channel connection is failing and pauses syncing to it, retrying safely once it recovers — so one OTA outage never cascades into a booking crisis
• Respects each OTA's own API rate limits automatically — no manual throttling needed
• Never double-imports the same reservation, even on retry

Push a rate update, and it fans out to every connected channel in parallel:

json { "roomTypeId": "king_std", "dates": ["2026-06-01", "2026-06-30"], "baseRate": 199.0, "restrictions": { "minStay": 2, "closedToArrival": false } }

Check the health of any channel connection at a glance:

json { "status": "success", "latency": 234, "lastSync": "2m ago" }

GDS Adapters (for corporate groups + travel agencies):

• Sabre — Hotel Availability API
• Amadeus — Cloud Services, Hotel Service V5
• Travelport — Apollo / Galileo, multi-GDS support
• Shared base session + PNR logic across all three

OTA Adapters (for direct consumer booking):

• Booking.com — largest direct OTA, API v3
• Google Hotel Ads — free booking links in search
• Expedia — Rapid API integration
• Airbnb — short-term rental
• VRBO — vacation rental
• Plus 10 more: Agoda, Hotels.com, TripAdvisor, Priceline, Hostelworld, Despegar (LATAM), Trip.com (China), Trivago, HRS (China), Kayak

Run the full night audit with one action, and get back a structured summary:

A dry-run mode validates every step without posting anything to the ledger, so managers can preview the close before committing it.

json { "status": "completed", "auditLogId": "AUDIT-2026-05-11-001", "businessDate": "2026-05-11", "totals": { "roomCharges": 18450.0, "taxes": 3321.0, "fees": 890.0, "penalties": 150.0 // Early departures, no-shows }, "occupancy": { "roomsSold": 87, "roomsAvailable": 100, "occupancyPct": 0.87, "adr": 212.07, // Average daily rate "revpar": 184.5, // Revenue per available room "oooRooms": 2 // Out of order }, "noShows": 3, "earlyDepartures": 7, "stayovers": 61, "housekeepingTasksCreated": 87, // One per expected checkout "gmBriefing": { "arrivalsCount": 26, "departuresCount": 31, "vipGuests": [ { "name": "Sarah Chen", "reservationId": "res_abc", "roomNumber": "701" } ], "occupancyForecast": 0.91, "nextBusinessDate": "2026-05-12" } }

20+ Sequential Reconciliation Steps:

1. Open batch 7. Post allowances 13. Post minibar 2. Post room charges 8. Post packages 14. Post incidentals 3. Post taxes 9. Post restaurant 15. Close AR 4. Post fees 10. Post telephone 16. Reconcile bank 5. Post penalties 11. Post laundry 17. Post city ledger 6. Reconcile payments 12. Post parking 18. Post GL 19. Generate report 20. Close date + HK tasks

Provider abstraction across 11 transports:

6 email providers (SendGrid, SES, Mailgun, Postmark, Resend, SMTP), 4 SMS providers (Twilio, Telnyx, Plivo, Vonage), and the WhatsApp Business API — all behind one templating layer with variable substitution:

json { "to": "guest@example.com", "templateId": "pre-arrival", "variables": { "guestName": "Sarah Chen", "roomNumber": "301", "checkInTime": "3:00 PM", "digitalKeyInstructions": "...", "wifiPassword": "Welcome2026" } }

The AI concierge classifies guest intent and either handles it or escalates:

json { "handled": true, "response": "Late checkout confirmed until 2 PM. $35 fee applies." }

If the request is too complex to auto-resolve, it's escalated to your helpdesk with the full conversation context attached.

Guest Lifecycle Communications:

Pre-Arrival (48h): Booking confirmation + digital key instructions + directions Check-In (0h): Room number + key delivery + WiFi + welcome Daily (8 AM): Weather + activities + restaurant hours Evening (6 PM): Tomorrow's weather + checkout reminder + late checkout offer Post-Departure (2h): Thank you + NPS survey + review request

The shape of a guest folio:

A folio follows the guest from pre-arrival deposit to post-departure settle — every charge in one running ledger:

json { "status": "open", "transactions": [ { "type": "room_charge", "amount": 199.0, "taxAmount": 35.82, "description": "Night 1" }, { "type": "restaurant", "amount": 85.0, "referenceId": "order_abc", "description": "Dinner" }, { "type": "minibar", "amount": 16.0, "description": "2 × beer" }, { "type": "spa", "amount": 120.0, "referenceId": "order_def", "description": "Massage" } ], "payments": [ { "type": "prepayment", "amount": 199.0, "method": "card", "reference": "pi_xxx" } ], "balance": 156.82 }

Restaurant, spa, and minibar charges arrive on the folio in real time as they happen — no manual entry, no clipboard, no reconciliation errors.

At checkout: charge the card, close the folio, notify housekeeping, and expire the digital key — one action.

Deterministic rule engine + demand forecasting:

Demand forecasting considers seasonality, local events, competitor comp-set rates, advance booking velocity, and cancellation patterns:

json { "demandLevel": "peak", "predictedOccupancy": 0.94, "compSetPrices": [219, 235, 199], "eventsNearby": ["Jazz Festival"], "recommendedRate": 349 }

Rate computation is deterministic and consistently fast, even during flash-sale traffic spikes:

json { "rate": 329, "appliedRule": "peak_event_adjustment", "reason": "Jazz Festival nearby" }