Hotel rooms priced the same regardless of demand, guests who leave with a complaint that never reached the right team, and booking fraud that settles before it's caught: these are the revenue and experience problems that AI addresses in travel and hospitality.
We build AI systems for hotels, OTAs, airlines, and travel agencies: dynamic pricing for hotels and flights, demand forecasting, personalised travel recommendations, AI-powered customer support for bookings and complaints, sentiment analysis on guest reviews, itinerary optimisation, and fraud detection for payment processing. Every system is scoped against your booking data and a specific revenue or experience outcome.
Dynamic pricing models that adjust room and fare rates based on demand signals, competitor pricing, and booking pace
Demand forecasts at the property and route level that let revenue managers make better inventory and rate decisions
Sentiment analysis on guest reviews that surfaces recurring issues before they compound into rating damage
Payment fraud detection that flags high-risk bookings before the stay, not after a chargeback arrives
RaftLabs builds AI systems for hotels, OTAs, airlines, and travel agencies that convert booking data into revenue and experience decisions. Dynamic pricing models adjust room and fare rates continuously based on booking pace, competitor pricing, and event calendar signals. Demand forecasting produces uncertainty-bounded forecasts that feed inventory allocation and group pricing decisions. Payment fraud detection scores each booking at point of payment using travel-specific patterns, reducing chargeback rates without increasing false declines on genuine travellers. Engagements are scoped at a fixed price after a discovery phase that maps your booking data and revenue management workflows to the specific AI capability being built.
Trusted by
Revenue that comes from reading demand before it peaks
Travel AI is most valuable when it converts the signals already in your booking data -- demand pace, search behaviour, review language -- into decisions your revenue and operations teams can act on before the opportunity or problem passes.
Capabilities
What we build
Dynamic pricing models
Pricing models for hotel rooms and fare classes that respond to booking pace, current occupancy, competitor rates, and event calendar signals. Output is a rate recommendation per room type or fare bucket per forward date. Where your PMS or channel manager has a pricing API, rates are pushed directly. Where they don't, recommendations appear in a revenue manager dashboard for approval. Built with rate floor and parity rule constraints. Calibrated to your demand patterns and competitive set.
The underlying model combines price elasticity estimation with a demand-response curve calibrated to your property or route: at what rate does booking pace accelerate, and at what rate does it stall? Price elasticity coefficients are estimated from your historical booking and rate data, segmented by room type, lead time bucket, and day-of-week pattern. Competitor rate data is ingested via channel manager APIs or rate shopping feeds (including OTA-sourced rate parity data) and normalised against your rate positioning. For GDS-connected properties and airlines, Amadeus, Sabre, and Travelport APIs surface availability and fare data from the wider distribution ecosystem, enabling competitive context beyond your direct channels. Hotel channel manager API integrations (SiteMinder, RateGain, Cloudbeds) allow automated rate pushes to OTA, GDS, and direct booking channels simultaneously. Rate floor enforcement and parity rule compliance logic is built into the recommendation engine so the model never outputs a rate that violates your parity agreements or undercuts your floor pricing.
Demand forecasting
Booking volume and revenue forecasts at the property, route, or market level over rolling 30-90 day horizons. Trained on your historical booking data, search query volumes, and external event signals. Outputs uncertainty-bounded forecasts that feed inventory allocation, group pricing decisions, and revenue budget planning. For hotels, forecasts at the room-type level. For airlines, forecasts by origin-destination and cabin class. Replaces spreadsheet-based occupancy and load factor projections with model-driven forward-looking estimates.
The forecasting model uses LightGBM gradient boosting as the primary estimator for its handling of irregular seasonal patterns, missing data, and mixed-type feature sets common in travel booking data. Inputs include historical booking pace curves (pickup by lead time window for the equivalent period in prior years), search query volumes from your platform (search-to-book conversion rate as a leading demand indicator), competitor pricing history, local event calendars sourced from Eventbrite or PredictHQ, and macroeconomic signals where relevant for longer-horizon forecasts. For NLP-based itinerary demand signals -- extracting travel intent from unstructured booking notes, group inquiry emails, or tour operator correspondence -- spaCy NER and transformer-based classification models parse origin, destination, travel party size, and date range from free text at ingestion. Forecast output includes point estimate plus confidence interval bands so your revenue management team can distinguish high-certainty near-term forecasts from wider-ranging long-horizon projections. Forecast accuracy is tracked against actuals using MAPE and symmetric MAPE by forecast horizon, with model retraining triggered automatically when drift exceeds calibrated thresholds.
Personalised travel recommendations
Recommendation models trained on your booking and search history data that personalise destination, property, and product suggestions for each logged-in traveller. Collaborative filtering uses the behaviour of similar travellers to generate recommendations. Content-based filtering recommends products with similar attributes to past bookings. For OTAs, personalises homepage and search result ranking. For hotel groups, surfaces relevant properties and room types for each returning guest. For travel agencies, supports itinerary proposals tailored to client preferences.
AI-powered customer support
Conversational AI for booking queries, change and cancellation requests, itinerary questions, and complaint first response. Trained on your booking system data, policy documentation, and historical support transcripts. Resolves routine queries -- booking confirmation, change fee calculation, check-in time queries -- without agent involvement. Escalates complaints and complex changes with full context passed to a human agent. Integrates with your CRM, booking engine, and ticketing system. Reduces average handling time on high-volume routine contacts.
Guest review sentiment analysis
NLP models that process guest reviews across OTA platforms, TripAdvisor, Google, and direct post-stay surveys to extract structured sentiment signals. Classifies feedback by property, stay date, room type, and topic -- cleanliness, F&B, service, value -- and tracks sentiment trends over time. Surfaces recurring complaint themes before they drive rating decline. Flags emerging issues for the property management team. Identifies which specific attributes drive positive reviews by segment so operations teams know where standards are translating into satisfaction scores.
Payment fraud detection
Classification models that score each booking payment by fraud probability using card data, device signals, booking lead time, velocity features, and traveller-payer mismatch signals. High-risk bookings are flagged for review or step-up verification before the booking is confirmed. Trained on your historical booking and chargeback data to detect travel-specific fraud patterns. Reduces chargeback rates and associated fees without increasing false declines on legitimate bookings from genuine travellers.
Which travel revenue or experience problem do you want AI to address?
Dynamic pricing, demand forecasting, fraud, or review analysis: tell us the specific outcome and we will assess which AI system delivers it and what your booking data supports.
We build itinerary optimisation models that solve the routing and sequencing problem for multi-destination trips: given a set of destinations, activities, and timing constraints, find the sequence that minimises transit time and maximises the guest's planned activities. Used by travel agencies building personalised itinerary proposals and OTAs adding a planning layer to their booking flow.
Dynamic pricing for hotels uses a combination of demand signals to recommend or set the optimal rate for each room type on each future date. The core inputs are booking pace data (how fast is inventory selling relative to the same date last year?), current occupancy, competitor rates from rate shopping data, local event calendars (a conference that fills the city will lift demand for a specific week), and historical demand patterns by day of week and season. The model outputs a recommended rate for each room category on each forward date. This replaces or augments the manual rate-setting process your revenue manager currently runs. Where you have a channel manager or PMS with a pricing API, the model can push rates directly. Where you don't, it presents recommendations through a dashboard for the revenue manager to review and approve. The model is calibrated to your rate floors, brand positioning, and any rate parity agreements. We assess your PMS data and historical booking history in discovery to determine the achievable pricing accuracy and the integration approach.
Demand forecasting for OTAs and airlines predicts booking volume by route, origin-destination pair, cabin class, and departure date window. Inputs include historical booking and ticketing data, search query volumes on your platform (search-to-book conversion rates reveal intent), pricing history, competitor schedule changes, and external signals such as economic conditions and travel restriction history. For airlines, forward-looking demand also incorporates corporate travel contract commitments and group booking history. Output is a demand forecast with uncertainty bounds that feeds capacity allocation decisions -- how much inventory to hold at each fare class -- and pricing strategy. The value of demand forecasting is not the point estimate but the confidence interval: knowing the range of likely demand allows inventory decisions to be made with measured risk rather than gut feel. We assess your booking data history and market data access in discovery.
Personalised travel recommendation models use a traveller's booking history, search behaviour, and profile data to predict what destinations, accommodation types, and travel products they are most likely to book next. Collaborative filtering approaches find travellers with similar behavioural profiles and use the bookings of similar travellers to generate recommendations for the current user. Content-based filtering recommends products similar in attributes to what the traveller has previously booked. For OTAs and hotel groups, this personalises the destination and property recommendations shown to each logged-in user rather than presenting the same featured properties to everyone. For travel agencies building itinerary proposals, the model can suggest activities, accommodation, and routing based on the client's past trip preferences. The model is trained on your booking and search data. It requires sufficient transaction history per user to personalise effectively, so for thin user histories, we use hybrid approaches that blend behavioural signals with preference data collected at sign-up.
Travel booking fraud detection is a classification model that scores each booking transaction by fraud probability at the time of payment. Features include transaction amount, card BIN and issuing country, billing address versus traveller nationality, device fingerprint, booking lead time relative to departure (fraudsters often book close-in to minimise detection time), number of cards attempted on the same booking session, and velocity signals (how many bookings from this card or device in the last hour). High-risk bookings are flagged for manual review or 3DS step-up authentication rather than processed automatically. The model is trained on your historical booking and chargeback data. Travel has specific fraud patterns that differ from general e-commerce fraud -- flight bookings used for mileage fraud, hotel bookings on compromised cards with intent to cancel -- and a model trained on your booking data detects these patterns more accurately than a generic fraud score. We assess your chargeback data history and payment processor integration in discovery.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope AI for Travel and Hospitality in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
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.
