AI Travel Price Intelligence & Booking Optimisation Platform
Predicts whether flight and hotel prices will rise or fall in the next 7–30 days — with 89% accuracy — so travellers and travel managers always book at the right time
Overview
Built for a US-based corporate travel management company handling $40M in annual travel spend across 200+ corporate clients.
The Challenge
Travel managers were booking on gut feel and getting consistently beaten by price movement, with no signal on when the optimal booking window would open.
What We Built
A prediction engine that monitors price signals across 180+ airlines and 500,000+ hotel properties and fires alerts the moment the optimal booking window opens.
Customizable by Design
Alert thresholds, supported airlines and hotel inventory, and corporate travel policy rules are all configurable per client, so the same engine serves an OTA, a travel management company, or an in-house corporate travel desk. The AI works both ways: travel managers get predictable spend forecasting across their portfolio, while individual travellers get booking-window alerts matched to their own trips.
What Sets This Apart
- •Corporate-policy-aware predictions that respect T&E limits, preferred vendors, and budget caps — not a consumer tool retrofitted for business travel
- •CFO-ready savings reports showing defensible dollars saved against a counterfactual, not just a headline accuracy percentage
- •Deep coverage of GCC carriers and OTAs — flyadeal, Saudia, Almosafer — where global price-prediction tools have almost no visibility
- •Disruption-aware rebooking that proactively re-prices and suggests the best option the moment a flight is delayed or cancelled, instead of leaving travelers to start over
Results
- •89% accuracy on price movement direction
- •Average $340 saved per booking vs. unassisted booking
- •$4.2M in travel savings across the client portfolio in Year 1
- •Covers 180+ airlines and 500,000+ hotel properties globally
Tech Stack
Key Outcome
89% price prediction accuracy · $340 saved per booking · $4.2M saved in Year 1