Vector database

You'll never operate a vector database yourself, but it's quietly doing the work whenever your platform matches an attendee interested in "sustainable packaging" with an exhibitor listed under "compostable containers." A conventional database needs the words to match; a vector database compares meanings, which is exactly what messy, free-text event data requires. Registrants describe themselves in their own words, exhibitors write product descriptions in marketing language, and no shared taxonomy connects them — vector search bridges that gap. For organizers, the commercial relevance is match quality: the better the platform handles inexact language, the fewer relevant connections slip through. The common mistake is treating "we use a vector database" as a buying criterion in itself. It's plumbing, not a feature — what matters is whether recommendations improve, which you should judge by testing real profiles from your event. One honest nuance: similarity isn't relevance. Two exhibitors can sit close together in vector space because their brochures use the same buzzwords, not because they solve the same problem. Platforms that combine vector similarity with structured signals — categories, buying intent, behaviour — beat those that rely on meaning-matching alone, and the difference shows up in your matchmaking complaints inbox.