In the travel industry, reliable information is everything—whether you’re surfacing destination insights, evaluating venue details, or curating activity recommendations. But with data coming from everywhere—static databases, user-generated content, vendor feeds, and now multiple AI models—the challenge isn’t just finding facts. It’s establishing reliable facts at scale.
Traditional sources like curated travel databases, guidebooks, and supplier catalogs have long been the go-to. They’re structured, vetted, and familiar. But they’re also static, slow to update, and often limited in scope. On the other hand, AI models—especially large language models (LLMs)—can synthesize vast amounts of information from across the web, including reviews, blogs, and real-time updates. The trick is not choosing one over the other, but integrating both to build a more dynamic and trustworthy picture.
This isn’t about cherry-picking facts for one hotel or one tour—it’s about processing thousands of records and establishing a level of reliability that holds up across the board. That means evaluating each source—whether it’s a legacy database or an AI-generated summary—for consistency, duplication, and accuracy. It also means understanding the behavior of each LLM: what kinds of errors it tends to make, how it handles ambiguity, and where its blind spots lie.
We’ve found that using multiple AI models in tandem, alongside traditional datasets, allows us to triangulate truth more effectively. If three models agree on a venue’s description and it matches supplier data, we can be reasonably confident. If one model diverges, we flag it. This comparative approach doesn’t eliminate errors entirely, but it reduces them to a level that’s at least as reliable as traditional sources—and often more current.
Of course, this requires real data research. You can’t just trust what’s returned—you need to analyse it. That means building workflows that track source provenance, detect duplication, and validate facts across systems. It also means investing in tooling that supports large-scale comparison and confidence scoring, so you’re not just guessing—you’re quantifying reliability.
In a fast-moving industry like travel, freshness matters. Static databases can’t keep up with changing hours, seasonal offerings, or new venues. AI can. And when paired with structured sources and rigorous validation, it becomes a powerful engine for scalable, trustworthy insights. The future of travel intelligence isn’t static—it’s layered, dynamic, and built on a foundation of smart integration.
AI: Building reliability
