AI travel recommendation engine development company
A recommendation is only useful if it’s instantly bookable. Most users arrive with zero search history, options depend strictly on dates and party size, and inventory changes by the minute. As an AI travel recommendation engine development company, COAX Software builds engines that learn what each traveler is after, check against live availability, and prove value in A/B tests before they take over your pages.
Industry leaders we work with
What an AI travel recommendation engine development company builds into your product
Discovery quizzes for first-time visitors
New visitors have no history, so a short-style quiz asks how they like to travel. ARRIVAL uses this approach to turn first visits into personal trip lists.
Similarity and behavior models
Trip descriptions, tags, and traveler behavior are turned into vectors and compared with FAISS similarity search. Travelers who love one trip see others that feel the same, even when the destinations differ.
Only bookable suggestions
Every recommendation is checked against live availability and price from sources like Nezasa, Katanox, and HyperGuest. Sold-out options never reach the screen.
Context that changes the answer
Travel dates, group size, budget, and season all shape what fits. A family in August and a couple in November get different suggestions for the same destination.
Add-ons at the right moment
Transfers, breakfast, spa treatments, and activities get suggested during checkout and before the trip. Each add-on matches the booking it accompanies.
Grounded “why this fits you” text
Language models write short explanations using only facts from your catalog. They describe a trip and never invent prices, dates, or availability.
Merchandising controls
Your team can boost high-margin trips, pin editorial favorites, and exclude products from recommendations. AI supports their decisions without replacing them.
Testing and measurement
Every placement runs against a simple baseline in A/B tests. Click-through, conversion, and revenue per visitor show whether the engine earns its place.
Pick the moment that matters
We choose where recommendations can move revenue first: the homepage, trip pages, checkout, or pre-trip emails.
Success metrics are agreed upfront.
One placement gets full focus.
Deliverable: placement and metric plan
Audit the catalog and signals
We check how well trips are described, tagged, and priced.
Available behavior data is reviewed for quality and consent.
Gaps get fixed before modeling.
Deliverable: catalog and data readiness report
Start with a strong simple baseline
Rules and popularity rankings go live first.
They set the bar any model has to beat.
Sometimes they’re enough for now, and we’ll say so.
Deliverable: baseline recommendations
Add models and bookability checks
Similarity models, quiz logic, and context rules are added.
Every suggestion passes a live availability check.
Merchandising controls go to your team.
Deliverable: recommendation engine in staging
A/B test against the baseline
The engine runs on a share of real traffic.
Results are compared on conversion and revenue.
It only expands if it wins.
Deliverable: A/B test report
Expand and keep learning
More placements and channels join.
Models are retrained as new bookings arrive.
Performance is reviewed every month.
Deliverable: rollout plan, monthly performance report
Numbers from our AI travel recommendation engine development company work
50,000
Travelers registered on ARRIVAL by launch day, each one a profile to personalize
20,000
Weekly active users discovering trips on ARRIVAL
200+
Trips booked through ARRIVAL’s own channel
170+
Properties in Stay Altered’s catalog across 45+ countries
40%
Profit growth at MICRM after hotels, tours, and activities moved into one cart
Our AI travel recommendation engine development toolkit
Guardrails around personalization
Why brands choose COAX Software as their AI travel recommendation engine development company
Bookable or not shown
Recommendations are tied to live inventory from the start. Travelers never fall for a suggestion that can’t be booked.
Day-one personalization without history
Discovery quizzes give new visitors relevant suggestions from their first session. ARRIVAL had 50,000 registered travelers by launch, all reachable with personal recommendations.
AI that supports your team
Our AI practice builds recommendation engines on established methods and keeps people in charge of key decisions. Your merchandisers decide what gets promoted.
Models have to beat a baseline
Each model must outperform simple rules in A/B tests before it expands. If rules work just as well, we’ll tell you and save you the cost.
FAQ
COAX Software does. As an AI travel recommendation engine development company, we build discovery quizzes, similarity models, live availability checks, and add-on suggestions for booking platforms, rental businesses, and tour brands.
Several AI firms do, but travel needs more than a generic model. COAX Software is an AI travel recommendation engine development company that connects every suggestion to real inventory and measures the business impact in A/B tests.
COAX Software does. Our AI travel recommendation engine development covers catalog audits, baseline rules, machine learning models, grounded AI descriptions, merchandising controls, and ongoing testing.
COAX Software can. We start with one high-value placement and a simple baseline, add models and bookability checks, and expand only when the numbers prove the engine is worth it.
COAX Software develops them around your catalog and travelers, using vector similarity, context rules, and quiz-based discovery. Privacy and consent come first, under ISO/IEC 27001-certified processes.
What we’ll do next?
1
Contact you within 24 hours
2
Clarify your expectations, business objectives, and project requirements
3
Develop and accept a proposal
4
After that, we can start our partnership
