How AI-powered attendee matching works, where it diverges from classic networking tools, and how it produces measurable business outcomes at real events — with Schedulo as the reference.
The biggest hidden cost of a corporate event is the wrong conversations. A CTO who spends two days at a summit but misses the three strategic introductions she was hoping for has just experienced a negative ROI event.
AI matching is how you replace intuition with data when answering "who should talk to whom?".
Most event apps treat matching as basic filtering. Two blind spots:
distribution channels" share no tags but talk about the same thing.
manager saying "investing in founders" carry symmetric tags but asymmetric needs.
A modern embedding model turns text into a vector. The closer two vectors are in cosine similarity, the closer their meaning is. Schedulo runs this with OpenAI's text-embedding-3-small model.
Schedulo uses gpt-4o-mini in JSON mode to extract a structured "who do you want to meet?" from each user's text. This goes beyond the symmetric similarity baseline.
Schedulo writes a bilingual reasoning line for every match:
"You share goals in insurance brokerage and reinsurance. A meeting could open up mutual collaboration opportunities around growing your corporate portfolios."
When embeddings can't be produced, a Jaccard tag-overlap fallback kicks in. When the LLM is unreachable, reasoning falls back to a deterministic template. The system stays up.
AI matching, with semantic similarity, asymmetric intent, and explainable recommendations, turns events from random card-trading into planned, high-value conversations.
AI matching, meeting briefings, sponsor ROI reports and more — in one command center. Let us walk you through it in a 30-minute demo.
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