AI Integration for Mews Contactless Check-in

AI integrates into Mews' contactless check-in by acting as an orchestration layer between the Mews API, external verification services, and the guest's mobile journey. The primary touchpoints are the POST /guests and POST /reservations endpoints for profile creation and status updates, the POST /companies/{companyId}/tasks API for internal coordination, and the webhook system for real-time event triggers like reservation.checkedIn. An AI agent listens for a new reservation's 'pre-arrival' status, initiating a sequence of automated steps that traditional workflows handle manually.

The core AI workflow involves three sequential, automated decisions: 1) Digital ID Verification, where the agent prompts the guest via the Mews Guest App or a branded web portal to upload a passport/driver's license, uses a computer vision service to extract and validate data against the guest.profile, and flags mismatches for human review. 2) Automated Room Assignment, where the agent queries the GET /rooms endpoint for available, clean rooms matching the booking's ratePlan and any stored preferences (e.g., high floor, away from elevator), applies business logic (e.g., upgrade eligibility), and assigns the room, updating the reservation.assignedSpaceId. 3) Keyless Entry Provisioning, where the agent triggers the creation of a digital key via the POST /accessCards API or calls a third-party lock system's API, sending the activated key directly to the guest's app. Concurrently, it can trigger personalized upsell prompts for late check-out or spa bookings via the POST /messages API, using the guest's stay history and real-time inventory.

For rollout, we recommend a phased, rules-governed approach. Start with a pilot segment (e.g., direct bookings for returning guests) where the AI agent operates in a 'human-in-the-loop' mode. All verification results, room assignments, and key provisioning actions are logged as tasks in Mews for front desk approval before final execution. This builds trust and creates an audit trail. Governance is managed through a configuration layer that defines which ratePlans or company segments are eligible for auto-assignment, sets fallback timers (e.g., if no action in 1 hour, escalate to a task), and contains the logic for handling exceptions like declined payments or special requests. The system's performance is monitored via webhook-driven logs, tracking metrics like time-to-check-in, auto-assignment acceptance rate, and upsell conversion, ensuring the AI is augmenting—not disrupting—operational reliability.

The integration is triggered by key events in the Mews API, primarily the reservation.confirmed and reservation.updated webhooks. Upon receiving a payload containing the guest's reservationId, the system orchestrates a multi-step workflow: First, it fetches the full reservation and guest profile data from Mews' reservations and customers endpoints. This data—including arrival time, room type, and any stored preferences—is then passed through a secure gateway to an AI orchestration layer. Here, a dedicated check-in agent evaluates the data against configured business rules (e.g., digital ID requirements, pre-authorization status) to determine the guest's eligibility for a fully contactless journey.

For eligible guests, the agent executes a sequence of tool calls: It may invoke a third-party digital identity verification service, with results logged back to the reservation's notes. Concurrently, it calls Mews' services API to attach pre-approved upsell offers (like late check-out) to the folio. Upon successful verification, the agent uses the commands API to trigger an automated room assignment, optimizing for operational constraints like room readiness from the housekeeping module. Finally, it provisions access by calling the integrated keyless entry system's API (e.g., ASSA ABLOY, SALTO) with a unique token and pushes a check-in completion notification to the guest via the Mews mobile API or a configured messaging channel.

Governance Note: All AI-generated actions, such as room assignments or offer attachments, are written as pending tasks to a dedicated audit log within the orchestration platform. This allows for human-in-the-loop review by front desk staff before final PMS commitment, if required by policy. The entire data flow is designed with least-privilege API credentials, and guest PII is never persisted in the vector database; context is held ephemerally in the agent's session. Rollout typically begins with a pilot segment (e.g., returning loyalty members) to validate workflow accuracy and guest satisfaction before scaling.

A production-ready integration with Mews for contactless check-in operates on a least-privilege API access model. Your AI agents should authenticate using dedicated service accounts scoped to specific Mews API endpoints—primarily guests, reservations, companies, and properties—to fetch booking data, verify digital IDs, and update reservation statuses. All PII (passport data, payment tokens) should be processed ephemerally; the AI system acts as a stateless orchestrator, never persisting sensitive guest data. Webhooks from Mews (e.g., reservation.updated) trigger AI workflows, while outbound calls to keyless entry systems (like SALTO or ASSA ABLOY) and payment gateways are made via secure, signed payloads.

Rollout follows a phased, property-first approach to manage risk and gather feedback:

• Phase 1 (Pilot): Enable AI-driven check-in for a single property and a controlled guest segment (e.g., pre-verified loyalty members). The AI handles digital ID capture and basic room assignment, but all actions are logged to a human-in-the-loop dashboard for review before finalizing in Mews.
• Phase 2 (Expansion): Activate automated room assignment and upsell prompts based on real-time inventory from the Mews API. Introduce automated keyless entry provisioning for successful check-ins, with failover to front desk workflows.
• Phase 3 (Scale): Roll out to additional properties, enabling cross-property preferences and advanced workflows like automated early check-in based on housekeeping status from Mews' rooms API.

Governance is enforced through a centralized audit layer that logs every AI-initiated transaction against the Mews API. This includes the original guest request, the data points evaluated (e.g., ID match score, upgrade eligibility), the action taken (e.g., room moved from 101 to 501), and the final reservation state. This traceability is critical for compliance, dispute resolution, and continuous model tuning. Furthermore, business rules—such as minimum age for check-in or blackout dates for upgrades—are maintained outside the AI model as a separate rules engine, ensuring policy control remains with operations teams.