Ulenia
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Ulenia/Client work/Maritime transport & tourism
ClientMaritime transport & tourism
SectorMaritime transport & tourism — AI architecture
DurationTo be specified
Ulenia teamAI architecture · Product · Data
Travel & Yield Management Agent

Optimising profitability and customer experience through AI and capacity management.

DP
Dynamic pricing
LFRT
Real-time cabin & parking loading factor
AOV
Personalised bundling offers
CM
Capacity management
§ 01

Challenge

Optimise profitability and customer experience in a context where combined offers, seasonal demand and physical capacity constraints interact constantly.

The core issue is not only pricing: it is the ability to orchestrate demand, cabin and parking capacity, accommodation availability and service bundles in real time.

§ 02

Solution

The proposed architecture combines three decision layers: personalised bundling, dynamic pricing and real-time capacity reading.

The goal is to support commercial decisions with a system able to simulate scenarios, recommend adjustments and generate coherent offers under operational constraints.

01
Travel Bundle Planner
An AI agent orchestrating real-time personalised offer creation to increase average order value.
02
Dynamic Pricing Recommender
An AI-assisted pricing engine analysing behavioural signals and enabling agile price adjustments.
03
Real-time loading factor
Real-time visibility on loading factor by crossing, cabin and parking to arbitrate remaining capacity.
04
Capacity Management
A digital twin of crossings to simulate occupancy scenarios and optimise naval capacity allocation.
05
Personalised bundling offers
Dynamic composition of Ferry + Accommodation + Services bundles, aligned with customer profiles and capacity constraints.
06
Commercial steering
Operational recommendations to trigger commercial actions based on demand, margin and loading factor.
§ 03

Value added

Yield management becomes more reactive to market variations, with pricing and bundling decisions connected to real-time loading factor.

Commercial teams can assemble complex offers instantly and with fewer errors, while decision-makers can simulate targeted interventions based on cabin, parking and crossing capacity.

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