AI Agents for Hospitality
A chatbot answers a guest's question about check-in time. An AI agent reads the booking record, checks whether early check-in is available, confirms the guest's room preference from their profile, sends a personalized arrival message, and updates the PMS, before the guest arrives, without a staff member involved. The difference matters in hospitality, where the cost of manual personalization at scale is staff time that could be spent on the guests standing in front of them.
Pre-arrival agents that personalize communication from guest profile and booking data
Reservation agents that handle date changes, upgrades, and special requests within policy
Review monitoring agents that draft responses and route escalations for approval
Upsell agents that identify upgrade opportunities and send targeted offers
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Your team spending hours personalizing pre-arrival messages when the information is already in the PMS?
Post-stay review monitoring eating front desk time that should go to guests actually in the property?
Plain answer
RaftLabs builds autonomous AI agents for hospitality workflows: pre-arrival guest communication, reservation amendments, post-stay review monitoring and response, upsell offers, housekeeping coordination, and loyalty interactions. Unlike chatbots that only answer questions, these agents take actions end-to-end within defined guardrails, integrating with property management systems and booking platforms. A first working agent launches in 10-14 weeks at a fixed cost, then you add more once it proves out.
What to remember
Meanwhile the same guest's profile already holds their room preference, their loyalty tier, and a note that they arrive early every stay. Nobody reads it, because reading it for every arriving guest is hours of front desk time that goes to the guests standing in the lobby instead.
An agent reads that record before the guest arrives. It checks whether early check-in is available that day, confirms the room preference, sends a personalized arrival message, and updates the PMS, without a staff member involved.
A chatbot answers a question. An agent completes the stay.
We build hospitality AI agents with defined scope, explicit escalation logic, and integration with your PMS and booking channels. Each agent handles one part of the guest journey well rather than many parts poorly. Agents are stateful, multi-step processes built on frameworks like LangGraph that call the PMS and booking channels directly, rather than just explaining what's possible. The timing matters: a large majority of US hotels report staffing shortages (American Hotel & Lodging Association), and agents absorb the repetitive guest requests and back-office follow-ups that would otherwise sit in a queue behind an understaffed front desk.
PMS integration is scoped during discovery, because that's where most hospitality projects encounter unexpected complexity: API coverage varies significantly by PMS and property configuration, so we confirm integration scope honestly rather than estimating generically.
Proof
RaftLabs has shipped production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE. The team that scopes your guest workflow is the team that builds it: no bait-and-switch, no offshore handoff after the contract is signed. A guest-journey agent rarely stands alone. It works alongside AI for hospitality for the personalization models, AI agent development for the orchestration discipline, and business process automation for the back-office workflows behind the front desk.
| Guest interaction | Rule-based chatbot | Hospitality AI agent | Human front desk |
|---|---|---|---|
| "What time is check-in?" | Keyword match returns canned text. | Reads the booking, states the guest's actual check-in, offers early check-in when the day allows. | Accurate, but only while the desk is staffed. |
| Move a reservation date | Out of scope, so it tells the guest to call. | Checks availability and rate rules, writes the change to the PMS, sends a new confirmation. | Handled, but it ties up the desk. |
| Reply to a new review | No access to review platforms. | Drafts a specific reply and holds it in a manager approval queue. | Often skipped on a busy shift. |
| 200 arrivals in one evening | Scales, but every message is generic. | Scales and stays personalized per guest profile. | Does not scale, so guests wait. |
| Complaint, VIP, or dispute | Loops or dead-ends. | Escalates to staff with the full conversation and booking context attached. | The right owner, once reached. |
The middle column is the whole point. A rule-based chatbot recognizes phrases; an agent reads state and acts on it; a person does both but cannot cover every arrival at once. The agent takes the volume that does not need a human and hands back the cases that do.
Everything on the left should already be true for your property. Even one thing on the right, and a chatbot or a manual process is the smarter first step.
A PMS with API access (Opera Cloud, Mews, Cloudbeds, Apaleo) that an agent can read booking data from and write updates back to.
A guest workflow your team runs manually at volume: pre-arrival messages, reservation changes, review responses, or upsell offers.
Booking policies clear enough to hand an agent, and budget for a build from $25,000.
A PMS with no API access, or a property configuration that can't expose booking data.
You want a chatbot that answers guest questions, not an agent that takes action inside your systems.
The workflow depends on judgment or a guest relationship the agent was never given context for.
What we build
Tell us the workflow you want to automate, your PMS, and the guest touchpoints involved. We'll scope what an agent can handle and give you a fixed cost.
How it works
We map the target workflow, your PMS's API coverage, and escalation logic. You leave week 1 with a written scope document and a fixed-price quote.
Booking policy rules, approval queues, and escalation conditions defined and confirmed with your operations team.
Agent workflow built against LangGraph, tested against real PMS and channel data every sprint.
Production deployment with escalation queues staffed and approval workflows active from day one.
Most hospitality agent projects fail on the same handful of problems. We scope for them before writing code, not after a guest gets a wrong answer.
The near-term shift is from a single guest-facing assistant to a set of narrow, coordinated agents behind the desk, each owning one workflow and handing off through defined queues. Emerging agent-to-agent standards (Anthropic's Model Context Protocol, and the interoperability work forming around it) point to agents that call the PMS, the channel manager, and the payment processor as tools rather than through brittle point integrations. We build toward that now by keeping each agent's scope narrow and its integrations explicit, so a property can add the second and third agent without rebuilding the first. The properties that win will not be the ones that automate the most; they will be the ones that automate the repeatable middle and keep humans on the moments that need a human.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
USAAll of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
Proof
Where you land in that range depends on scope, not negotiation:
Cost is driven by the number of systems integrated, the complexity of the policy rules the agent must apply, and the number of workflows in scope. We scope every project before pricing it.
What it costs
A focused agent covering one workflow, or a multi-agent system across pre-arrival, reservations, reviews, and upsell, scoped and priced before any development starts.
Most properties start with one agent on one workflow, then add more once it's proven in production. You know the entry price before we build anything.
Starting investment
Starts at $25,000
Priced by project once PMS integration scope is confirmed during discovery. 10-14 weeks to live for a single workflow agent, with room to add more agents once the first is proven.
No hourly billing
Once we scope your first agent, that price is locked in writing. No hourly billing, no surprise invoices for work outside the agreed scope.
Human approval
Review responses and guest communications outside defined scope always route to a human approval queue before anything goes live. The agent drafts, it never publishes autonomously.
Useful next steps
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Read moreA chatbot answers a guest's question. An AI agent completes a workflow. When a guest asks a chatbot about upgrading their room, the chatbot explains the upgrade process. When an AI agent handles an upgrade request, it retrieves the booking record, checks real-time availability, confirms the rate difference is within its authority, updates the PMS, sends an updated confirmation, and logs the change, all without a staff member involved. Agents operate as stateful, multi-step processes that call external systems and take actions, with a complete action history for every workflow.
We integrate with PMS platforms that expose an API for booking data retrieval and updates, including Opera Cloud, Mews, Cloudbeds, Apaleo, and RoomKey. For properties using channel managers (SiteMinder, D-EDGE, Staah), we can integrate at the channel manager level where PMS API coverage is limited. Review platform integrations use Google Business Profile, TripAdvisor Management Center, and Booking.com's Property API, or a review aggregator such as ReviewPro. PMS integration scope and API access requirements are confirmed explicitly during discovery.
Guest data handling follows minimum necessary access principles: each agent retrieves only the data its specific workflow requires. Data is encrypted in storage and transit, with access controls scoped to each workflow. For UK and Ireland properties, we document data flows, identify the lawful basis for processing under GDPR, and provide the data processing record required for accountability compliance. LLM API providers process only the data the specific task requires, typically booking metadata and review content, and we confirm the provider's data processing terms cover those data types before any guest data flows to the model.
A focused hospitality AI agent covering one workflow, one PMS integration, defined escalation logic, and a manager approval queue typically runs $25,000 to $55,000 and delivers in 10-14 weeks. A multi-agent system covering pre-arrival communication, reservation amendments, review monitoring, and upsell offers with integrations into PMS, channel manager, review platforms, and guest messaging typically runs $55,000 to $130,000. Cost is driven by the number of systems integrated, the complexity of policy rules the agent must apply, and the number of workflows in scope.
Work with us
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
