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Person Embeddings

Domain-specific embeddings and rerankers for human compatibility. Trained on expert-labeled data to match people on what actually matters.

Human Compatibility Matching

Retrieval Accuracy (NDCG@10)

52.4%
Gemini
56.1%
OpenAI
60.8%
Jina
64.5%
Qwen
69.2%
Voyage
84.3%
Jean
Embeddings

General-purpose embeddings treat resumes like documents. Ours are trained on who actually got hired, retained, and promoted.

Core Product

Person Search

We train custom embedding models on expert-labeled data. The model learns what “good match” means in your specific domain.

Hires that stay. Matches that convert. Connections that compound. Standard embeddings reward keyword overlap. Ours reward compatibility.

Recruiting & Hiring

Rank candidates by predicted tenure and performance, not resume keywords.

Dating & Social

Match on behavioral compatibility and relationship success signals.

Marketplace Matching

Connect buyers to sellers, mentors to mentees, founders to investors. Optimized for conversion.

Context Layer

Jean Memory

A persistent identity layer for AI applications. Users sign in once, and every interaction across your product builds a memory that follows them.

This is how we gather the context that flows into person embeddings. Without memory, you're matching on static profiles. With it, you're matching on living, evolving representations of real people.

5-Line SDK

Drop-in React components. No vector DB setup, no retrieval pipeline to build.

Cross-Session Identity

Your AI picks up exactly where it left off, across devices, sessions, and applications.

Privacy-First

User-scoped data with granular access controls. Users own and can delete their memory.

Infrastructure

Embedding Adapter

A learned mapping between embedding spaces. Upgrade models, query across silos, or federate data between organizations without re-indexing a single vector.

Switching embedding providers normally means re-encoding your entire corpus. Our adapter recovers 95-99% of native retrieval accuracy at a fraction of the cost and zero downtime.

Model Upgrades

Move from an old embedding model to a new one without re-encoding billions of vectors.

Cross-Silo Queries

Search across partner datasets without exchanging raw data. Query-only federation.

Legacy Integration

Bridge legacy vector stores to modern infrastructure without operational disruption.

Ready to build?

We deploy custom infrastructure tailored to your domain.