Pushing
Boundaries.
We build representations of people and intent that go beyond surface-level text matching, enabling precision compatibility at scale.
Technical Writing
Deep dives on matching infrastructure, embedding models, and the technical decisions behind our platform.
The State of AI Memory 2026
Industry overview on the current landscape, from RAG to long-context windows and beyond.
General Personal Embeddings
An exploration of how deep representation learning can be applied to people, and why the logical endpoint is trusted infrastructure for personal embeddings.
Research
Research Areas
Person Embeddings
Modeling complex human traits, intent, and historical behavior to map compatibility across disjoint domains.
Domain Outcomes
Training dual-encoder models on successful, real-world outcomes rather than text similarity.
Behavioral Context
Capturing the longitudinal signals that turn static profiles into living representations precise enough to match on.