Union doesn't guess. It computes. Every introduction is the output of a graph-theoretic system that maps 20 million relationships, scores trust, optimizes paths, and delivers the shortest warm route between you and the person you need.
Every person in Union's system exists as a node in a directed, weighted graph. Every relationship — professional, social, transactional — is an edge with properties: strength, recency, context, and trust score.
This isn't a contact list. It's a living topology of human connection. Edges decay over time. New signals refresh them. The graph breathes.
Union continuously scans public professional networks, company registrations, board appointments, funding rounds, speaking engagements, published works, and social signals.
Raw data is ephemeral. What persists is the computed relationship signal — a normalized score that answers: how connected are these two people, and through what context?
When you tell Union who you're looking for, we translate your natural language into a set of graph constraints. "A CTO who's scaled a B2B SaaS from 1M to 10M ARR" becomes a subgraph filter: role=CTO, company_stage=growth, revenue_range=[1M,10M], sector=B2B_SAAS.
The candidate set emerges not from keyword matching, but from structural position in the graph. People who occupy similar topological positions to successful matches are weighted higher.
Dijkstra finds the shortest path. Union finds the warmest. Our modified path algorithm weights edges not just by distance, but by trust score, relationship recency, and introduction willingness.
A 4-hop path through trusted colleagues outperforms a 2-hop path through weak acquaintances. Every time.
Every introduction Union makes passes through a five-stage pipeline. Each stage is a gate. If any stage fails the trust threshold, the introduction is rerouted or held.
Ingest signals. Index new nodes and edges. Refresh decay scores.
Translate your request into graph constraints. Build candidate subgraph.
Rank candidates by composite trust, relevance, and path warmth.
Compute optimal warm path. Verify each intermediary's willingness.
Orchestrate the introduction. Monitor follow-through. Score outcome.
Union's trust model computes a multi-dimensional score for every edge. It's not just "do these people know each other." It's: how well, in what context, how recently, and would they vouch for each other?
Trust propagates through the graph but decays with distance. A first-degree trusted connection carries more weight than a third-degree connection through strangers. The decay curve is exponential, not linear.
Every successful introduction feeds back into the trust model. Every ignored introduction decays it. The system learns.
Union is computing 847K edges per second. Yours are waiting.
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