AI for Travel, Retail & Loyalty
Retailers, airlines, hospitality groups and loyalty programmes in the Gulf serve some of the most mobile, most digitally demanding customers anywhere. Eurisko builds the travel, retail and loyalty use cases that keep them engaged: personalized promotion and offer optimization, basket affinity and next-best product, loyalty redemption and miles optimizers, ancillary offer recommenders, an AI travel concierge and disruption recovery assistant, receipt intelligence for warranties and returns, and marketplace seller quality scoring, across the GCC, the wider Middle East and Lebanon.
Every recommendation is measured on incremental impact rather than clicks, every assistant is grounded in your catalogue, policies and benefits, and every experience is designed for Arabic and English customers alike.
Relevance that pays for itself, in the store, in the app and in the air.
Promotions in this region are generous and frequent, and most are wasted on customers who would have bought anyway. The retailers and programmes that pull ahead are the ones that measure incremental response, personalize the offer, the incentive and the moment, and keep the customer’s trust by never over-contacting them.
Eurisko builds the recommendation and optimization layer that makes this possible: uplift models for promotions, basket and product affinity for cross-sell, value-normalized redemption for loyalty, and grounded assistants that turn a catalogue, a policy or an itinerary into a conversation.
The same foundation serves the loyalty coalition, the airline, the mall group and the e-commerce marketplace, because the questions are the same: who should hear what, through which channel, and what did it actually change.
Eight travel, retail and loyalty use cases, ready to prove on your data.
Each one is scoped to be proven on masked order, booking or programme data and shipped into the app, the campaign tool or the contact centre your customers already use.
Personalized promotion & offer optimizer
Estimates the incremental response to each offer rather than targeting customers who would buy anyway, and optimizes audience, incentive and frequency under campaign budget and eligibility constraints.
Basket affinity & next-best product
Learns product and category relationships from baskets, then personalizes the next-best item or bundle from recent behaviour, with both a global affinity view and a per-customer recommendation view.
Loyalty redemption & miles optimizer
Normalizes the value of every redemption option, accounts for availability and preferences, and recommends the best points, cash or mixed strategy, explaining the trade-offs and planning toward a target reward.
Ancillary offer recommender
Uses trip context, traveller history, fare class and prior behaviour to rank relevant ancillaries and bundles, balancing relevance, margin and customer fatigue.
AI travel concierge & trip spend planner
Combines itinerary, budget, benefits, partner offers and preferences into a grounded day-by-day plan, and replans conversationally when the budget or the itinerary changes.
Travel disruption recovery assistant
Reads a disruption, checks rebooking policy and insurance or card benefits, compares the recovery options and gives the traveller a step-by-step plan with the compensation checklist.
Receipt intelligence, warranty & returns
Extracts merchant, date, totals and line items from receipts, matches products to warranty and return rules and builds a digital receipt wallet with a clear next-step checklist.
Marketplace seller risk & quality scoring
Combines operational, financial and customer-feedback signals to score seller quality and risk, surfacing drivers, emerging deterioration and a review queue rather than an automatic sanction.
Measure the lift, ground the assistant, respect the customer.
Recommendation use cases share one engine: customer, product and transaction features feed affinity, propensity and uplift models; business rules apply margin, inventory, eligibility and frequency caps; and a ranking step chooses the offer, the channel and the moment. Results are evaluated with control groups so the number reported is incremental revenue, not attributed revenue.
Assistant use cases, the concierge and the disruption helper, follow the grounded pattern Eurisko uses everywhere: retrieval from your policies, catalogue and benefits, generation constrained to what was retrieved, citations the customer can open, and a hand-off to a person for anything the content does not cover.
Document use cases, receipts and seller evidence, use the same extraction stack as our banking work: classification, OCR, line-item parsing and product matching with confidence scores and a review queue.
Built for Ramadan peaks, Gulf travel corridors and two languages.
Commerce in the region moves with its calendar: Ramadan and the Eids, back-to-school, national days, the summer exodus from the Gulf and the pilgrimage seasons that reshape airline and hospitality demand. Our models are trained with these seasonalities and evaluated across them, so an offer that worked in a quiet month is not assumed to work during the peak.
Customers also expect every touchpoint in Arabic and English, from the promotion copy to the concierge conversation and the receipt wallet. Generated content is evaluated per language against your brand voice, and benefits, policies and catalogues are retrieved in the language the customer is using. For Lebanon’s retailers and the diaspora that shops with them, multi-currency pricing and cross-border loyalty are handled as ordinary cases.
A process built on evidence.
Discovery & success metrics
A workshop with the business owner, the data owner and the technology lead fixes the decision to improve and the number that proves it.
Data readiness
We profile the sample, masked or synthetic, and say plainly what it can and cannot support before anything is built.
Prototype
Model, prompt, retrieval or optimization logic plus a working demo your team can click through, not a slide.
Evaluate & play back
Accuracy, groundedness, fairness and cost are measured against an agreed test set and reported with their limitations.
Productionize
Integration, security review, MLOps and monitoring inside your perimeter, scoped only once the evidence supports it.
Frequently asked questions.
How is this different from the recommendation engine in our e-commerce platform?
Platform recommenders optimize clicks on one channel. Our use cases optimize incremental value across channels and respect your margin, inventory, eligibility and contact-frequency rules, with control groups that report what the recommendation actually changed. They can feed your existing platform rather than replace it.
Can the concierge or disruption assistant book or change flights?
It prepares everything and, where you choose, executes through your booking systems with the customer’s confirmation. In a first phase it typically plans, explains eligibility and drafts the claim or request, and hands the action to an agent or the self-service flow.
Does loyalty optimization work across coalition partners?
Yes. Value normalization is designed for programmes where points can be earned and redeemed across retailers, airlines, hotels and banks, and recommendations are explained per option so members understand the trade-off they are making.
What data do we need to start?
For retail, masked order and basket history, the product catalogue and promotion history. For loyalty, earn and burn rules, the redemption catalogue and member activity. For travel, booking records, policies and benefit documents. Synthetic data can cover any part that cannot be shared.
Does our data have to leave the country or our data centre?
No. For banks, telecoms and government entities in Saudi Arabia, the UAE, Kuwait, Oman and Lebanon we deploy inside your private cloud or on-premises, use open-weight models when data cannot leave the perimeter, and start with masked or synthetic samples so no customer record is needed to prove the idea.
Where do we start if we want more than one use case?
Pick a small portfolio rather than isolated ideas: one quick win, one strategic differentiator and one operational efficiency case, ideally sharing one data foundation and one evaluation framework. We run a short selection workshop with your business, data and technology leads to shortlist by value and data readiness.
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