Generative AI: Travel and Hospitality

Generative AI for travel and hospitality

Travellers ask the same questions at every hour, in every language, and expect an answer that matches the booking they actually hold. We build AI agents that read live availability, rates and reservations from your PMS, CRS or booking engine, quote only your real policies, and hand over to staff when money, safety or a complaint is involved. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

Discuss a travel AI use case Travel and hospitality software

Key highlights

We build guest messaging, voice reservation, trip planning, disruption handling and review analysis agents for hotels and travel companies.
Agents answer from live PMS, CRS, GDS or NDC data through tool calls, so prices and availability are never made up.
Refund, compensation and fare rules come only from your approved policy text, and anything outside it goes to a person.
Multilingual by default, across web chat, WhatsApp, SMS, email and phone.
A first use case, such as pre arrival guest messaging for one property group, usually reaches a guarded pilot in 8 to 12 weeks.

Why AI projects stall in travel and hospitality

Chatbots are not new in travel. Most were scripted, frustrating and disconnected from the booking, and the generative versions bring a new risk: promising something the policy does not allow.

A chatbot that invents policy. In Moffatt v. Air Canada in 2024, a Canadian tribunal held the airline responsible for a bereavement fare refund its website chatbot had wrongly described. Whatever the agent says, the brand owns.

Systems that do not talk to each other. Reservations live in the PMS, rates in the channel manager, loyalty in a CRM and flights in a GDS. An assistant that cannot read them can only give generic answers.

Front desk and call centre overload. Staff spend their shifts on questions about check in times, parking, transfers and booking changes, while the guests who need real help wait on hold.

Disruption days break everything. A cancelled flight or a storm produces thousands of rebooking requests at once. Rules for rebooking, vouchers and passenger rights compensation are complex, and agents apply them inconsistently.

Reviews nobody has time to read. Thousands of reviews and survey comments across properties contain the operational fixes that matter, but they are read only when someone complains to the general manager.

Content at catalogue scale. Descriptions for properties, rooms, tours and destinations are needed in many languages and keep going out of date, so listings on OTAs and the brand site drift apart.

Use cases

Generative AI use cases we build for travel and hospitality

Each agent works from live data and approved policy, and each one has a clear handover point to your staff.

Guest messaging and concierge agent

A tool using agent on WhatsApp, SMS and web chat that reads the reservation from OPERA Cloud, Mews or your PMS, answers pre arrival and in stay questions, sells upgrades and add ons, and opens tickets for housekeeping or maintenance.

Voice reservation agent

A phone agent that checks availability and rates through your CRS or booking engine, takes new bookings and changes, and transfers to a reservations agent with a summary when the guest asks for a person or the request is complex.

Trip planning and booking assistant

A conversational planner that searches your real inventory, flights over GDS or NDC APIs, hotels and activities, assembles an itinerary and hands the customer a priced, bookable basket instead of a list of ideas.

Disruption and rebooking agent

Reads the disruption, the passenger's booking and your rebooking rules, proposes options, explains entitlements from your approved policy text, and escalates compensation and exceptions to a human.

Review and feedback intelligence

Classifies reviews and survey comments by property, department and issue, summarises trends for each general manager and drafts on brand replies for staff to approve.

Multilingual content generation

Generates and translates property, room and tour descriptions from structured facts, checks them against your brand guide and pushes approved copy to your CMS and distribution channels.

How we deliver

From one property to the whole portfolio

01
Use case discovery and data readiness
We rank use cases by guest impact and risk, then check the data: PMS and CRS API access, policy documents, FAQ content, message history and which channels are in scope.
02
Evaluation harness and prototype
We build a golden set from real guest conversations and edge cases such as cancellations and refunds. The prototype is scored on correctness, policy compliance and handover behaviour on every change.
03
Guarded pilot with staff in the loop
One property or one queue goes live, with staff able to see and take over any conversation. Flagged answers feed the test set and the policy text.
04
Production rollout with monitoring
Rollout across properties, brands and languages with dashboards for containment, handover rate, guest satisfaction, cost per conversation and drift alerts.

AI governance for travel and hospitality

Travel AI handles passport details, payment data and personal preferences, and its answers can create obligations. Governance covers both.

PCI DSS v4.0 scope reduction GDPR and UK GDPR CCPA and CPRA EU AI Act transparency EU261 and UK261 passenger rights Accessibility (WCAG 2.2) PII redaction Data residency by region Self hosted model option

Card data never reaches the model. Payments are taken through tokenised payment links or IVR secure capture handled by your PCI DSS compliant provider. The AI sees only tokens and booking references, which keeps it out of cardholder data scope. Passport and loyalty details are redacted before any external model call.

The agent can only promise what policy allows. Refund, cancellation, compensation and fare rules are retrieved from approved policy text and applied through tools that check eligibility, not decided freely by the model. Exceptions and goodwill gestures go to staff, and every promise is logged.

Guests know they are talking to AI. Agents identify themselves as AI, as EU AI Act transparency rules require from August 2026, offer a human at any point, and keep a full transcript with sources so any disputed answer can be reviewed.

Models and tooling we use for travel AI

Model choice is made per use case after testing on your conversations. The integrations are what let the agent act on real bookings.

Anthropic Claude OpenAI GPT models Google Gemini Llama and Mistral self hosted LangGraph agents Oracle OPERA Cloud and Mews APIs Amadeus and Sabre APIs IATA NDC integration WhatsApp Business Platform Twilio and SIP voice agents pgvector and Elasticsearch Langfuse tracing and evals

FAQ

Common questions

Can the AI agent make and change real bookings?

Yes, through your existing APIs. The agent calls your PMS, CRS, booking engine or GDS to check availability, quote rates and create or modify reservations, with confirmation steps before anything is charged. It never invents prices or availability.

How do you stop the agent from promising refunds we do not offer?

By separating policy from generation. Refund and compensation rules come from your approved policy text and eligibility checks run as tools, so the model explains a decision rather than making it. Anything outside the rules is handed to staff, and the evaluation set tests these cases on every release.

Is guest data safe?

Yes. Card data is handled by your payment provider and never reaches the model, PII is redacted before external calls where needed, and we use enterprise model endpoints with no training on your data in your chosen region, or self hosted models in your own cloud.

How long does a travel AI project take?

Usually 8 to 12 weeks to a guarded pilot for one use case and one channel. That covers discovery, PMS or CRS integration, a test set from real conversations and a pilot at one property or queue.

Should we buy a hotel chatbot or build our own?

Buy if a product supports your PMS and your channels and you are happy with its limits. Build when you run several systems, need your own policies and loyalty rules, or are a travel platform shipping AI to your own customers. We also extend bought tools with custom agents and integrations.

How is a travel AI project priced?

Usually as a fixed price discovery and prototype phase, then a pilot and rollout scoped from measured results. Running costs depend on conversation volume, channels and model choice, and we estimate them before you commit. See our pricing page for engagement models.

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