A travel AI is only as good as the inventory it can access. Before writing a single line of the Roavo product interface, we spent significant time mapping the data landscape — understanding what sources exist, how fresh they are, and what it takes to access them reliably.

The requirements are specific: real-time flight availability and pricing, not cached batch exports; hotel inventory with accurate room-level availability, not property-level guesses; and APIs that can handle query volume without rate-limit failures degrading the user experience.

The travel data ecosystem is fragmented in ways that are not obvious from the outside. Major airlines distribute through GDS networks but also maintain direct APIs with different coverage and pricing logic. Low-cost carriers often skip GDS entirely. Hotel inventory is split between large OTA aggregators, direct property connections, and wholesale bed banks — each with different freshness guarantees.

Getting the data layer right matters more than most people assume. The quality of inventory data — how fresh it is, how accurately it reflects actual availability, how gracefully it handles edge cases like codeshares and dynamic pricing — is a direct upstream determinant of recommendation quality. A reasoning model built on stale data reasons about the wrong things.

Establishing the right data access is one of the key milestones on our path to beta. We are in active conversations with providers and will share more once agreements are in place.