AI Agent Development Services for Hospitality
AI agents for hospitality that complete the stay, not just answer.
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
Start with what is not working.
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.
What to remember
- Agents are stateful, multi-step processes calling the PMS and booking channels directly rather than just explaining what's possible.
- PMS integration (Opera Cloud, Mews, Cloudbeds, Apaleo) is the highest-risk component of any hospitality agent build and is scoped explicitly during discovery.
- Manager approval queues gate every review response before publication - the agent drafts, it never publishes autonomously.
- A focused single-workflow agent is scoped after discovery and delivered at a fixed price; a multi-agent system across pre-arrival, reservations, reviews, and upsell expands from there.
- Guest data handling follows minimum necessary access, with each agent retrieving only the data its workflow requires, encrypted in storage and transit.
A guest asks what time check-in is. The chatbot answers. Then nothing happens.
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.
AI agents that act, not just answer
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 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
- shipping production software, including hospitality booking platforms with PMS integration
- Since 2015
- RaftLabs delivery record
- average client rating across delivered projects
- 4.9/5
- Clutch, verified reviews
- agents handle guest requests around the clock, escalating anything outside their defined scope to your team
- 24/7
- Every hospitality agent build
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 agent development for the orchestration discipline and business process automation for the back-office workflows behind the front desk.
Chatbot, agent, or front desk: the same guest request, three outcomes
| 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.
An agent pays off when the workflow is repeatable and your PMS can talk to it.
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.
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.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Choose a maintained product when it handles the core guest service operations workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
- 02
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
- 03
Build
Own the system when guest service operations shape margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
- 04
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
What we build
Hospitality agents for the guest journey
Pre-arrival guest agent
At a defined interval before arrival, the agent retrieves the booking record, reads guest profile preferences (pillow type, dietary requirements, loyalty tier, previous stays), and generates a personalized pre-arrival message via email, SMS, or WhatsApp. Responses within scope are handled directly; anything requiring judgment escalates to the front desk with full conversation context.
Reservation management agent
Handles date changes, room upgrades where inventory supports it, and special requests within the property's booking policy, without staff involvement. The agent checks rate amendment terms and real-time availability, then either applies the change to the PMS or explains why it can't. Requests requiring a rate override or involving a dispute route to the reservations team with context pre-loaded.
Review monitoring and response agent
Monitors new reviews on a 30-60 minute cycle across Google Business Profile, TripAdvisor, and aggregators like ReviewPro, and drafts a response acknowledging the specific feedback mentioned, not a generic template. Every drafted response routes to a manager approval queue before publication, the agent never publishes autonomously. Reviews mentioning a safety concern or legal claim flag for immediate manager attention rather than standard queuing.
Upsell and upgrade agent
Identifies upgrade and add-on opportunities from booking data, room type, rate paid, availability of higher categories, loyalty tier, and sends targeted offers 7-10 days before arrival when conversion is highest. Positive responses trigger PMS confirmation automatically; declines are logged with no further outreach on that booking.
Housekeeping coordination agent
Builds the daily housekeeping priority list from departure and arrival schedules, distributes it at shift start, and flags rooms running behind expected readiness, blocked by maintenance, or needed early for an early check-in. Stay-over service requests from the guest app log directly to the task list, with anything unusual flagged for supervisor approval.
Loyalty program agent
Handles routine loyalty interactions, points balance, tier status, standard reward redemption, missing points claims where the stay record is locatable, directly against the loyalty platform API. Claims requiring manual review route to the loyalty team with account details and the specific reason attached.
Have a hospitality AI agent project?
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
From scope to live agent
- Week 101
Workflow and PMS scoping
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.
- Weeks 2-302
Policy and approval design
Booking policy rules, approval queues, and escalation conditions defined and confirmed with your operations team.
- Weeks 4-1003
Connect the agent and its integrations
We build the agent workflow and test it against real PMS and channel data every sprint.
- Final 2 weeks04
Launch and monitoring
Production deployment with escalation queues staffed and approval workflows active from day one.
Pitfalls we plan around
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.
- Read access without write access
- Some PMS APIs let you read a booking but not update it. We confirm read-versus-write coverage per endpoint during discovery, so an agent is never promised an action the API cannot perform.
- Stale availability
- Inventory can change between the moment the agent reads it and the moment it writes. We commit changes through the PMS booking transaction, not a cached copy, so the agent never double-sells a room.
- Invented policy
- An unconstrained model will make up a cancellation or refund rule to sound helpful. We bind policy answers to the property's actual rules and route anything outside them to staff.
- Tone-deaf review replies
- A safety complaint or a bereavement does not want an automated reply. Reviews flagged for safety or legal content skip the draft queue and go straight to a manager.
- Guest data reaching the model
- Each agent sends the LLM only the booking metadata its task needs. We confirm the provider's data-processing terms cover those fields before any guest data flows to the model.
- Double-messaging
- A guest getting the agent's SMS and a staff email about the same request erodes trust fast. We assign one owner per touchpoint so the guest hears one voice.
Where hospitality AI is heading
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 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
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
Charles E.
Entrepreneur at Aggie Technologies
Proof
Adjacent hospitality work: booking automation with live PMS integration
- 01
Booking and keyless check-in for a serviced-apartment operator
City Break Apartments (Dublin). Not an agent build, but the same integration surface a hospitality agent depends on: reading and writing booking state against a live PMS and automating a guest touchpoint at scale.- PMS integration for real-time booking and availability sync
- RMS Cloud
- growth in self check-ins after the mobile app launched
- 7x
- staff hours per week returned to the team
- 20+
- 02
Perceptional replaces static surveys with AI-led interviews that adapt in real time
We built Perceptional, an AI interview platform for product managers that replaces static surveys with adaptive conversations and delivers structured summaries within 48 hours.- from concept to launch
- 12 weeks
- time to usable insights
- 48 hours
- 03
Pause reduces mindless app opens by 40% without blocking or timers
Pause intercepts the habit loop before it completes. One brief prompt before you open Instagram or YouTube cuts mindless sessions by 40% (client-reported), without restricting access or triggering resentment.- reduction in mindless app opens
- 40%
- App Store rating
- 4.6/5
- countries with active users in first 60 days
- 15+
Where you land in that range depends on scope, not negotiation:
- Single-workflow agent
- One workflow, one PMS integration, defined escalation logic, and a manager approval queue.
- Multi-agent system
- Pre-arrival communication, reservation amendments, review monitoring, and upsell offers, with PMS, channel manager, review platform, and guest messaging integrations.
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
Priced before development starts, scoped before we start.
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 is proven in production.
Starting investment
Starts at $10000
Priced by project once PMS integration scope is confirmed during discovery, with room to add more agents once the first is proven. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
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.
Work with us
Tell us where the work is stuck.
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.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
A 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.
The worst thing an AI can do in hospitality is guess: a wrong refund promise or a made-up WiFi password destroys trust faster than a slow reply. Explicit business logic has to be built in; the model understands language, not your business. Every rule in the guardrail set exists because the opposite happened in a real deployment.
The hotel that forgets you is about to lose you. Masked OTA relay addresses mean the OTA-booked guest is a stranger with a borrowed name in hotel systems. Guest recognition needs a unified guest-data layer, not another channel tool. The write-back test: can the chatbot write back to the PMS? Ask which objects it may read and write.
The honest metric: hours released and reinvested into guest contact, not positions removed. Costs scale with conversation length, not flat licenses. The agent handles check-in and out info, house rules, amenities, WiFi, and booking info independently; it hands off maintenance, leaks and lockouts, safety, complaints, refunds and compensation, non-standard cancellations, and incomplete info.
A focused hospitality AI agent covers one workflow, one PMS integration, defined escalation logic, and a manager approval queue. A multi-agent system covers pre-arrival communication, reservation amendments, review monitoring, and upsell offers with integrations into PMS, channel manager, review platforms, and guest messaging. 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. Every project is scoped and priced at a fixed cost before development starts.