Problem Solved
Many travel operators rely on unscalable manual processes. Agents draft itineraries by hand, triage enquiries in arrival order, and allocate marketing budgets to channels with unclear booking attribution. The data required to resolve this, such as past bookings, enquiry outcomes, and campaign spend, remains fragmented across separate tools.
We can help you build custom machine learning systems to bridge this gap. For example, an itinerary drafter handles the initial processing, allowing agents to refine a draft rather than starting anew. An enquiry-scoring model prioritises leads with the highest booking probability. An attribution model connects campaigns directly to bookings, ensuring budget allocation follows empirical evidence. We train each system on your data, validate it against your booking metrics, and transfer operational control to your team.
Agents consequently dedicate their time to qualitative judgement and closing. The business can manage a higher volume of trips without proportional headcount increases.
Two of these capabilities have a dedicated page: AI product recommender systems for the matching and itinerary work, and A/B testing and experimentation for the test-and-learn loop that proves each change before it ships.
For enquiry scoring without a custom build, our sister company Akilima offers a hosted lead-scoring API you can integrate directly.
