AnyIntAccelerating AGIPlain routing sends every request to one fixed model. Intelligent routing first understands the request, then picks the best-fit model and endpoint under quality, cost, latency, privacy, and safety constraints.
The numbers below are local demo data used to illustrate the product advantage: without lowering the quality threshold, simple requests go to cheaper models while high-risk or hard tasks stay on dedicated lanes.
Versus routing everything to a fixed strong model, it keeps the quality threshold while cutting unnecessary large-model calls.
Every request goes to the same model or a set of static weights, so even simple tasks burn an expensive model.
Support, code, privacy, and long-doc requests are all treated the same, with no differentiation.
All traffic goes through one lane — no signal analysis and no policy branching.
Quality is acceptable, but even the simplest tasks burn the most expensive compute.
Keep one complete routing chain: pick a task type on the left, see the matched rule and policy in the middle, and land on different model pools and endpoints on the right.
Click a task type to see which signals this kind of request extracts first.
Merge signals into a decision vector, then hit concrete rules by priority.
complexity = easy AND cache_similarity > 0.9The matched policy binds pre, selection, and post plugins.
The selector balances quality, cost, latency, and deployment boundaries across the candidate pool.
Real intelligent routing isn’t random dispatch — it’s a configurable orchestration layer: it profiles every request with multi-dimensional signals like domain, complexity, context, and safety, then composes them into reusable routing rules across quality, cost, latency, and compliance goals, so every call lands precisely on the best-fit model and endpoint.
When authz + domain + language match, allow only the private model, enable PII redaction and audit logs, and turn off caching.
When complexity + embedding + keyword match, check the semantic cache first, then escalate step by step from low-cost models.
A fact-check signal triggers HaluGate: first decide whether verification is needed, then run span detection and explanation to cut wasted checks.