Where teams put the model decision layer to work.
The same primitive — a policy sent with the call — solves several production problems. Pick the one that hurts.
Cut model spend
Use the cheapest model that passes the floor — gemini-3.5-flash at $0.018 instead of a pinned gpt-5.5. Illustrative.
Handle provider failures
Fallback is policy, not retry code — on a timeout the router advances to the next model that still passes your rules.
Learn moreRemove hardcoded model names
Route by requirement, not a model string — a tools floor, a price ceiling, a bench_intelligence score, never a literal gpt-5.5.
Enforce per-customer rules
Different tenants, regions, and plans carry different policies — compiled per request, no dashboard, no redeploy.
Learn moreTrace every decision
See why the winner won and why each rejected model failed — by the rule that filtered it, with a fingerprint to replay it.
Learn moreRoute multi-step tasks
Each step gets its own policy: classify, draft, verify, return. A workflow is just policies composed into one bounded call.
Learn moreOne decision layer, many workloads.
We do not ship a workload-specific product per box. The mechanism is the same in every one — a policy your backend generates and sends with the call, evaluated filter → rank → select → fallback over the live catalog.
Whatever the workload, the primitive underneath is the same: policy is the thing you send, and the cheapest model that passes your rules answers — with a trace to prove it.
Find your problem, then send a policy.
Cost, reliability, hardcoded names, tenant rules, traces — one primitive sits under all of them. Start with the use case that hurts most, or join the waitlist and we'll help you wire it in.