AI-Powered Destination Recommendation Engine for a Luxury Travel Advisory

Overview

Built a destination recommendation engine for a luxury travel advisory that matches client preferences — travel style, past trips, budget range, and stated interests — against a curated database of destinations and experiences the advisory represents. Advisors use the tool's suggestions as a starting point for client conversations rather than a fully automated booking flow, since the advisory's value proposition rests on personal expertise, not self-service. The model was trained on the advisory's own historical client preference and booking data rather than generic travel trend data, so recommendations reflected the advisory's actual curated network of properties and experiences. This gave newer advisors on the team a way to draw on the firm's collective expertise from day one. Newer advisors at a luxury travel advisory had no efficient way to draw on the firm's collective expertise when researching destination options for a client, relying entirely on their own individual experience. We built a recommendation engine matching client preferences against the advisory's own curated destination and experience database, trained on the firm's historical client preference and booking data, positioned as a conversation starting point rather than a self-service booking flow. We trained the model on the advisory's own historical client preference and booking data rather than generic travel trend data, since the firm's real value was in its own curated network. Senior advisors reviewed the tool's early suggestions against their own judgment before it was rolled out to newer team members. Newer advisors cut the time spent researching destination options per client by an estimated 30%, letting them draw on the firm's collective expertise from their very first client conversations.