Networking is the promise behind most events, but the reality often depends on luck and time. You arrive with a goal-meet potential partners, find the right vendor, talk to people who understand your challenges-and then spend hours navigating long lists, vague profiles, and "maybe relevant" conversations. When an event is small, that can still work. When it scales to thousands of participants and dozens or hundreds of exhibitors, the classic approach starts to break down: too many options, too little context, and not enough time.
That is the gap run.events set out to address with the Event Intelligence Cloud and its new AI Matchmaking and AI Expo Agent capabilities. The development of these features was co-funded by the EU through the BEFuture Acceleration Programme, enabling run.events to invest in a system that treats networking as something that can be improved through intelligent interpretation of data rather than left to chance.
The core idea behind AI Matchmaking is simple: instead of asking people to manually search for "the right contacts," the platform can suggest connections that are likely to be relevant-while also explaining why they are relevant. In practice, this means the system looks at what participants say about themselves, what they are trying to achieve, and what they are offering. It then identifies potential links across several types of relationships. Sometimes that relationship is peer-to-peer: two attendees whose roles, interests, or current challenges complement each other. Sometimes it is attendee-to-exhibitor: a participant searching for a solution and an exhibitor whose offering fits that need. And sometimes it goes one level deeper, matching someone not just to a company, but to a specific product or service within an exhibitor's portfolio.
Just as important as the match itself is the context that comes with it. A recommendation without explanation is easy to ignore and hard to trust. That's why the matchmaking output is designed to provide more than a name and a profile link. Each suggested connection can include a relevance score and an understandable rationale-what the overlap is, what the shared topic or business need appears to be, and why a conversation could be worthwhile. To make the first step easier, the system can also suggest short conversation starters, not as marketing copy, but as a practical way to begin an exchange with the right framing.
AI Expo Agents become especially relevant for expos and sponsor-driven environments, where time is the most limited resource. Exhibitors don't just want "more scans" or "more traffic"; they want the right conversations. By mapping attendee needs to exhibitor offerings-and by recognizing signals that indicate buying relevance-the agents can surface higher-value meeting opportunities earlier and more reliably. That can reduce the manual effort involved in prospecting, filtering, and scheduling, and it can help exhibitors spend their booth time on conversations that have a clear purpose.
None of this removes the human element from events-nor should it. The point is not to "automate networking," but to lower the friction that prevents good networking from happening. In practice, event teams and participants still need control, visibility, and the ability to curate. That's why these capabilities are built to be used in an operational way: recommendations can be reviewed, refined, and acted upon, rather than treated as a black box. In other words, the system supports decisions-it doesn't replace them.
AI in events is often discussed in abstract terms, but matchmaking is one of the areas where outcomes are measurable. The relevant questions are straightforward: Do participants get to meaningful conversations faster? Do exhibitors spend less time chasing irrelevant leads? Do meetings convert into follow-ups? The Event Intelligence Cloud's new AI Matchmaking and AI Expo Agents are designed as a practical answer to those questions-grounded in the day-to-day reality of how business events actually work.