AI agents for hospitality that complete the stay, not just answer.

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 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.

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.

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.

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 (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

What our clients say

Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

Testimonial 1 of 1: Charles E.

Charles E.

Entrepreneur at Aggie Technologies

USA

All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!

Proof

Adjacent hospitality work: booking automation with live PMS integration

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.

Bella Skin Institute runs a fully automated loyalty program with no vendor lock-in

We built a gamified mobile loyalty platform for a medical spa, enabling patients to earn points with built-in urgency mechanics and redeem rewards, supporting a business model where patient lifetime value depends on returning every 3-6 months for cosmetic treatments.

RaftLabs grows organic traffic 30% after migrating its website to a headless CMS and launching 3,000 programmatic pages

RaftLabs migrated from Webflow to Sanity CMS to run a programmatic SEO strategy at scale. Zero downtime. 3,000+ pages launched. 12,000 monthly visitors moved without traffic loss. Organic traffic up 30% in the first year.

See all projects

Where you land in that range depends on scope, not negotiation:

Single-workflow agent, $25,000-$55,000

One workflow, one PMS integration, defined escalation logic, and a manager approval queue, delivered in 10-14 weeks.

Multi-agent system, $55,000-$130,000

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.