Precision Matching.
Standard embeddings match keywords. We build infrastructure that matches people, intent, and compatibility across billion-scale datasets.
Human-to-Human Matching
Problem: Superficial Similarity
Whether it's dating, hiring, or networking, platforms often map people by superficial keywords rather than true underlying compatibility.
Solution
Our embeddings capture deep psychological and behavioral traits, allowing platforms to match individuals based on outcome probabilities and true affinity.
Hyper-Personalized Recommendation
Problem: Disconnected Context
Traditional recommender systems treat users as disconnected clicks, struggling to recommend the most relevant products or content across different contexts.
Solution
By mapping users and items into a shared geometric space, we can accurately predict preferences and match the right person to the right item at billion-scale.
Agentic Matching
Problem: Brittle Agent Routing
Agents increasingly need to find the right counterparts, tools, products, or humans to complete tasks, but keyword routing and hand tuned heuristics break down as the space of options grows.
Solution
Jean embeds agents, tools, products, and people in the same semantic space, so any agent can match to any other entity based on true compatibility and expected outcomes.