Neural Environment
Model routing
Model routing lets ISAAC pick the right engine for the task while preserving one product experience, one policy layer, and one evidence trail.
Request -> task + risk classification
Policy -> model route + context budget
Result -> verifier -> trace + usage measurement
Bigger does not always mean better
Large frontier models are useful for some synthesis tasks, but many production workflows are narrower. They need precision, predictable behavior, low latency, and controlled cost.
ISAAC can route routine, domain-specific tasks to smaller specialized models or adapters and reserve larger models for work that actually needs broad reasoning.
Cost-aware routing
Routing is one of the main levers for reducing token waste. ISAAC can limit context, avoid unnecessary retries, select a cheaper capable model, and record when a more expensive model was justified.
The private-beta goal is to measure tokens per successful workflow, not just raw tokens consumed. That makes efficiency visible to operators and business owners.
No silent trust fall
A route decision should be visible. Steward should be able to show selected model, reason code, source context, verifier status, and any degraded-mode behavior.
That transparency is what lets clients swap models over time without rebuilding their product or losing auditability.