Memory & Translation
Infrastructure.
Different embedding models speak different languages. We build the translators that allow them to talk to each other, and the memory systems that let them learn.
The Latent
Disconnect.
As intelligence scales, it fractures. We are solving the fundamental disconnects that prevent AI models from functioning as a cohesive system.
Amnesia
The Persistence Problem. Models reset after every session. We provide the state layer for continuous identity.
Incompatibility
The Translation Problem. Embeddings drift and diverge. We build adapters to map across latent spaces.
Generalization
The Fidelity Problem. General models fail at specific tasks. We engineer domain-outcome embeddings for precision.
Infrastructure Matrix
Jean Memory
A persistent, cross-session memory layer that allows LLMs to retain user context and identity across disparate applications.
- Long-term Identity
- Cross-session Retrieval
- Privacy Governance
Embedding Adapter
Infrastructure to map old embedding spaces to new ones without re-indexing petabytes of vector data.
- Zero-downtime Migration
- Dual-index Bridging
- High-fidelity Translation
Outcome Vectors
Embeddings fine-tuned on conversion events and business outcomes, surpassing general-purpose models for high-value retrieval tasks.
- Outcome-based Training
- Contrastive Learning
- Graph Integration
Latent Bridge
A secure protocol for sharing intelligence between companies without sharing raw data.
- Shared Interlingua
- Query-only Federation
- Privacy-preserving Mapping
Behavioral Identity
A real-time user state engine that merges high-frequency interaction signals with deep semantic history.
- Behavioral Tokenization
- Latent Semantic Fusion
- Identity Centroids