Fusing Signal
with Semantics
A hybrid representation engine that merges high-frequency behavioral signals with deep semantic understanding.
The best of
both worlds.
Narrow predictive models (XGBoost, RecSys) excel at capturing discrete signals like dwell time or clicks but lack semantic understanding. General purpose LLMs understand language but are often blind to specific user context.
The Solution: Our Dual-State Identity engine acts as a "Centroid," ingesting raw behavioral logs, tokenizing them into vectors, and fusing them with semantic embeddings. This creates a complete user representation that is both precise in its prediction and holistic in its reasoning.
Behavioral Tokenization
Extract discrete metadata (location, device, interactions) and encode them as high-dimensional vectors.
Semantic Generalization
Leverage the reasoning capabilities of LLMs to create generalized representations of human intent.
Dual-State Synthesis
A unified representation that straddles the line between specific behavioral prediction and general semantic understanding.