AI Matchmaking at B2B events: what it really does and how to evaluate vendors

Almost every event platform now advertises “AI matchmaking”. The phrase does a lot of marketing work and very little explaining — which makes it hard to tell a system that genuinely produces better meetings from one that has relabelled a keyword filter. If you are choosing event matchmaking software, the useful question is not “does it have AI?” but “how does the AI decide, how much control do I keep, and how will I know it worked?”

This guide explains what AI matchmaking actually does, how the recommendation logic works, what separates a strong system from a weak one, how the main market approaches differ, and the exact questions to ask a vendor before you commit.

What is AI matchmaking?

AI matchmaking is the use of an algorithm to recommend the most relevant people for each participant to meet at an event — and, in the better systems, to help schedule those meetings — based on profile data, stated objectives, declared buy/sell intent, and on-platform behaviour. It replaces the old model of scrolling a long attendee list and guessing, with a ranked set of suggestions the participant can act on.

It is not magic, and it is not a black box that “knows” who you should meet. It is a recommendation engine: the quality of what comes out depends entirely on the quality of what goes in and the logic that ranks it.

How it actually works

A matchmaking engine draws on four kinds of input:

Profile data: role, company, industry, and what the participant offers or is looking for.
Stated objectives and intent: what the person says they want from the event, including explicit buy-side or sell-side intent.
Behaviour on the platform: sessions viewed, profiles opened, meetings requested, content saved. Signals of real interest, not just declared interest.
Feedback loops: accepted and declined meetings that teach the system what “relevant” looks like for this event.

The engine scores every possible pairing on relevance and surfaces the strongest matches to each side. The best systems then close the loop by turning a good match into a booked meeting — suggesting a slot, handling the request, and placing it on both agendas. A recommendation that never becomes a meeting is a metric, not a result.

What separates a good matchmaking system

Four things distinguish a system that produces meetings from one that produces a long list:

Recommendation quality. Behaviour- and intent-based matching beats static profile tags. Tag-only systems match on what people wrote in a form; behaviour-based systems learn from what they actually do.
User control. Participants should be able to steer their own matches — filter, express intent, accept or decline — rather than being handed a fixed list. Control raises acceptance rates.
Integrated scheduling. Matching and scheduling should live in one flow. If the recommendation and the calendar are separate tools, meetings leak between them.
Transparency and configurability. You should understand, at least in principle, why a match was suggested — and be able to tune the logic to your event’s goals, vertical, and buyer/seller dynamics.

The main approaches in the market

“AI matchmaking” describes several genuinely different strategies. Understanding them helps you match a vendor to your event rather than to a slogan:

Intent-based matching. Some platforms — Brella is a clear example — build the match around explicit buy/sell intent, optimising for the quality of each meeting over raw volume.
Community- and content-led matching. Others, such as Swapcard, use AI across the whole experience — suggesting sessions, content, and exhibitors — to turn an event into an ongoing community, with networking as one part of that.
Proprietary-engine, high-scale matching. Specialists like Grip position around the raw power of a proprietary engine generating very large numbers of recommendations, aimed at big meeting programmes.
Configurable, behaviour-based matching. Platforms with in-house algorithms — LetzFair among them — analyse real-time behaviour and let the matching logic be tuned to the event’s vertical, objectives, and buyer–seller dynamics.

None of these is universally “best”. An intent-heavy hosted buyer programme, a large community conference, and a niche vertical trade show reward different approaches. The point of evaluation is fit.

Questions to ask a matchmaking vendor

A confident demo is not evidence. Ask each vendor:

“What signals does your matching actually use?”: profile tags only, or intent and behaviour? Make them be specific.
“How much control does the participant have over their own matches?”: can they filter, express intent, and decline?
“Is scheduling built in, or a separate tool?”: confirm a match can become a booked meeting in one flow.
“Can the logic be configured for my event’s vertical and goals?”: or is it one fixed model for everyone?
“What acceptance and no-show rates do comparable events see?”: ask for real numbers from a similar event.
“How do you measure meeting quality, not just quantity?”: a million recommendations mean nothing if the meetings do not happen or do not convert.
“Can I see the matchmaking reporting a real organiser received?”: not a mock-up.

How to measure success

Judge a matchmaking system on outcomes, not features. The metrics that matter:

Meeting acceptance rate of suggested matches, how many became requested and accepted meetings.
Meetings actually held: booked meetings that took place, not just scheduled.
No-show rate: the gap between booked and held; a quiet killer of perceived value.
Meeting quality score: post-meeting ratings from participants on relevance and usefulness.
Downstream pipeline for exhibitors and buyers, the meetings that turned into opportunities.

If a vendor cannot help you measure these, they are selling recommendations, not results.

Common mistakes to avoid

Buying on “has AI”. Every platform claims it. The differentiator is the signals and the control, not the label.
Ignoring scheduling. Great matches that never get booked are wasted.
Optimising for volume. More recommendations is not better; more accepted, high-quality meetings is.
Skipping the reference call. Acceptance and no-show rates from a comparable event tell you more than any feature list.

Where LetzFair fits

LetzFair’s matchmaking is built in-house and behaviour-based: the algorithm analyses real-time behaviour to suggest high-quality connections, and — because the development is owned rather than licensed — the matching logic can be iterated around each event’s buyer–seller dynamics, objectives, and vertical. Matching and scheduling sit in one flow, so a strong match becomes a booked meeting with slots, locations, and follow-ups handled automatically, not left to the participant.

The aim is the outcome the category promises but often misses: meetings that are relevant, that actually happen, and that participants rate as worth their time. To see how the matching and scheduling work together, explore LetzFair’s event platform or request a demo.

Frequently asked questions

What is AI matchmaking at events? It is the use of an algorithm to recommend the most relevant people for each participant to meet, based on profile data, stated objectives, buy/sell intent, and on-platform behaviour — and, in stronger systems, to help schedule those meetings. It replaces manually scrolling an attendee list with ranked, actionable suggestions.

How does event matchmaking software actually work? It scores every possible pairing of participants on relevance, using profile information, declared intent, and behavioural signals such as sessions viewed and profiles opened. It surfaces the strongest matches to each side and, in integrated systems, turns an accepted match into a booked meeting on both agendas.

What makes one matchmaking system better than another? Recommendation quality (behaviour and intent beat static tags), participant control over their own matches, scheduling built into the same flow, and the ability to configure the logic for your event’s goals and vertical.

How do you measure whether matchmaking worked? Track meeting acceptance rate, meetings actually held, no-show rate, post-meeting quality scores, and downstream pipeline for exhibitors and buyers — not the raw number of recommendations generated.

Is AI matchmaking the same as a networking app? Not quite. A networking app is the participant-facing space for messaging and agendas; AI matchmaking is the engine that decides who each person should meet. The best platforms combine both, so recommendations lead directly to booked meetings.

What questions should I ask a matchmaking vendor? Ask what signals the matching uses, how much control the participant has, whether scheduling is integrated, whether the logic is configurable, and what real acceptance and no-show rates comparable events achieved.