Flight Search

AI accelerates flight search with caching, price prediction, and conversational booking agents.

Business impact

• Search latency — Lower latency improves user experience and reduces bounce rates during flight searches
• Booking conversion rate — Accurate and timely flight data increases the likelihood of users completing bookings
• User engagement — Faster and relevant search results keep users engaged longer on the platform
• Infrastructure cost — Caching reduces expensive real-time API calls, lowering operational expenses

Data requirements

• Historical flight price and availability data (Numeric) — Used for training predictive models to forecast price trends and booking windows
• Real-time flight schedules and status APIs (Structured) — Provide live data for accurate search results and availability updates
• User search and booking behavior logs (Structured) — Inform machine learning models to personalize and optimize search results
• Natural language queries (Text) — Enable conversational AI to interpret user flight search requests

AI methods and techniques

• Predictive AI — Forecast flight prices and optimal booking times using historical and real-time data
• Generative AI — Generate natural language responses and conversational flight search interactions
• Agentic AI — Autonomously perform flight searches and bookings via MCP servers and APIs

AI models and model families

Claude, GPT-4o, Gemini 2.5, Llama 3, Hermes 2 Pro

Industries

Real-world evidence

12 documented case studies on record.

Companies using this: Expedia Group, Fare Boom, Google, Hopper, Kayak, Kiwi, Sabre Corporation, Skyscanner, Turkish Airlines, Wingie Enuygun, Wingie Enuygun Group.