Industries Travel & Hospitality
Easier way tobook your next stay.
AI Engineering and software development for travel companies.
Demand moves hourly, shops across a dozen sites before it converts, and the cost of a booking doesn't stop at checkout. We build the pricing, inventory, and service systems underneath travel and hospitality products, including the ones that decide what happens when the trip goes wrong.
Ruma TravelLoyalty Point AI Booking Platform
A loyalty point booking platform built end to end. A wallet of thirty-plus loyalty programs, two travel data providers folded into one result set priced against those balances, and an AI assistant that plans a trip from a sentence.
- 2
- Travel data providers integrated
- 30+
- Loyalty and wallet programs
- 800+
- Flight and hotel results per search
Why travel companiestrust us with delivery.
An oversold room rarely draws a complaint. The traveller just books with somebody else next time. This is why we pay special attention to the most sensitive parts of the system.
Inventory expires at midnight
An unsold room or seat is not carried forward, it is gone. Pricing becomes an optimisation against a clock: rates move continuously, and every channel holding that inventory has to see the change in time.
Demand is seasonal, weekly, and hourly at once
A conference, a school holiday, and a competitor's flash sale all move the same night's price. A model trained on last year was trained on a different world, so the forecast has to be retrained and watched, not shipped once.
The review is part of the product
Reputation compounds into rate and occupancy months later, and most of it is recoverable: a complaint handled during the stay rarely becomes a one-star. The engineering is in noticing while the guest is still there.
Distribution is a tax on every booking
Commission, parity clauses, and channel managers syncing on their own schedule mean the same room is sold in six places at slightly different truths. Reconciling them is unglamorous work that pays for itself on every direct booking.
Service happens mid-trip
A cancelled leg at 2am in a language you do not speak is where loyalty is decided. What saves it is a support layer that already holds the itinerary, which is a data problem long before it is a service one.
What travel demandsfrom engineering and AI.
01Pricing that respects the constraints
Rate parity, contracted corporate rates, and minimum stays bound every price a system can propose, and a revenue manager has to understand a recommendation before accepting it. Optimisation that ignores those is technically correct and operationally unusable, so the constraints are where the real problem lives.
02Margin from ancillaries
Rooms and seats sell at market price; upgrades, bags, transfers, and experiences do not, which is where the margin actually sits. Capturing it means ranking which offer goes in front of which traveller at which point of the trip, with far better economics than another point of conversion on the base fare.
03Irregular operations under control
Cancellations, overbooking, and weather are normal here, not exceptional, and they land on the operation at its busiest hour. Automated rebooking, proactive notification, and a policy engine that decides what to offer whom turn the worst part of the job into the one that generates the most goodwill.
04Clean content and inventory data
The same property arrives from three suppliers with different names, amenity lists, and photographs, and none of them map cleanly. Deduplication, mapping, and content enrichment are unglamorous work, and they set the ceiling on everything search and personalisation can do.
Real-life stories of triumph.
Get In Touch
Integrating multiple travel providers sounds straightforward until you discover that every API behaves differently. We spent considerable time normalizing data and handling edge cases so users would experience a seamless search instead of inconsistent results. That investment greatly simplified future feature development.
Nortik’s Impact
The Ruma travel platform end to end: flight and hotel search aggregated from two providers into one comparable result set, a wallet holding thirty-plus loyalty programs, the booking flow on top of both, and the administrative console the platform is run from.
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Frequently asked questions
Everything travel teams usually want to know before automating pricing and service. Not here? Ask us directly.
Yes. That covers the property and central reservation systems, the channel manager, and the GDS or supplier APIs underneath them. Rate and availability updates go back through the systems that own them rather than around the side.
Most of the engineering is in reconciling what those systems each believe is true at a given moment, which is also where the avoidable revenue leaks are.
Two of our own builds sit on that layer: Ruma folds two travel data providers into one priced result set, and YGO runs its booking sites and whitelabel partner storefronts off a single frontend estate.
Recommendations come with the reasoning and the constraints applied, and you decide how much authority the system has: advisory, bounded auto-apply within a corridor, or full automation on segments where you've seen it work.
Revenue managers who can see why a rate moved will let the system run. Ones handed an unexplained number override it, and the model never gets a fair test.
For itinerary questions, changes, and the long tail of repeat queries, in the traveller's language, yes. The answers stay grounded in your policies and booking data instead of free-associating.
Disruption, compensation, and anything involving money moving get handed to a person with the context assembled. The escalation boundary is a design decision we make explicitly, not a fallback.
The peak is the design point. Search and availability are the load-bearing paths, so they get the caching, the headroom, and a degradation plan that keeps booking working even when the recommendations or enrichment layers are shed.
We test against your actual peak shape rather than an average, because the average day was never the risk.
Personalisation runs on what the traveller consented to, with consent state travelling alongside the data rather than checked once at the front door, and residency and retention decided before the data model.
Payment data stays inside the PCI boundary of the processor that already handles it. We don't move it somewhere new to make a feature easier.
You do. Code, trained models, pipelines, and documentation are yours, along with the infrastructure access to run them without us.
We hand over a codebase your own team can pick up. Nothing we build is designed to keep you dependent on us.