Task-Specific
Vector Optimization
Two-tower models trained on outcome graphs to surpass general purpose embeddings in specific verticals.
Disentangling intent
from semantics.
Standard embeddings treat all content equally, failing to distinguish between style, intent, and outcome. In high-stakes verticals like hiring, fraud detection, or reciprocal recommendations, semantic similarity is not enough.
The Solution: We train dedicated "Two-Tower" models that learn separate embedding spaces for different entities (e.g., Job Seekers vs Recruiters) and align them based on successful outcomes, not just text overlap.
Outcome Alignment
Optimize vectors to predict successful interactions (clicks, hires, sales) rather than just linguistic similarity.
Reciprocal Modeling
Handle asymmetric relationships (e.g., A wants B, but B doesn't want A) by projecting entities into distinct "Tower" spaces.
Data Flywheel
Every interaction refines the embedding space, creating a self-reinforcing loop of improving relevance.