Platform Technology
The technology that enables Day 0 model support through vector embeddings and zero-shot capability matching.
Traditional LLM routers are rigid classifiers trained on historical logs. They cannot generalize to unseen models without complete retraining cycles.
When DeepSeek-V3 or Llama 4 launches, classifier-based systems require:
Routey eliminates retraining through Dynamic Vector Routing—projecting prompts and models into a shared embedding space for zero-shot capability matching.
Prompts are encoded into a 768-dimensional embedding space that mathematically represents semantic intent, task complexity, and domain specificity.
Models receive capability embeddings through automated benchmark analysis—even Day 0 models map to the same semantic space in ~2.3 seconds.
Vector similarity matching selects the optimal model by geometric proximity in capability space—no classification boundaries, no retraining required.
Unlike traditional routers that memorize model pairs, Routey maps both prompts and models into a unified 768-dimensional vector space—enabling geometric routing decisions based on capability proximity.
"Explain quantum computing to a 10-year-old"
Projected into shared capability space
Vector Proximity
Matching
Nearest neighbor in embedding space (<5ms)
claude-sonnet-4.5
Geometrically guaranteed best match
Requires: Training data, GPU hours, manual validation
Requires: Nothing. Zero-shot capability matching.
Route to DeepSeek-V3, Llama 4, or any future model the second it's released—zero downtime.
Unlike NotDiamond, no need to upload proprietary evaluation datasets—works out of the box.
See why each model was chosen through cluster similarity scores—full transparency.
768-dimensional dense vectors
BERT-based semantic encoding
Cosine similarity clustering
Threshold: 0.85+ for routing decisions
2.3 seconds average
Zero user intervention required
<5ms decision time
Real-time cluster matching
