Send model traces from whatever you already log with. We audit them and show you what the intelligence layer would save: cached tokens, better routes, and what a model of your own would look like.
Every request routes to the cheapest model that clears your quality bar. The decisions come from your observed traffic, per agent and per prompt, not from a leaderboard: simple prompts go to cheaper models, hard ones to stronger.
When a switch wins, we tell you, with the numbers: one support agent moved from fable-5 to gpt-5.6-luna and kept quality while cutting cost 87% and latency 92%. New models are evaluated against your traffic the day they ship. Turnkey per-prompt optimization, if you want it.
Share of the last 1k requests
your model38%
fable-522%
gpt-5.6-sol14%
haiku-4.511%
glm-5.38%
qwen3.8-27b7%
The mix shifts as cheaper routes prove out.
02
Caching
Agent traffic repeats itself. The system prompt, the tool schemas, the few-shot examples, and most of a retried plan are the same tokens, run after run.
We catch opportunities to improve cache hit rate across your traces, and show what they would save. Turn it on and repeated tokens come back at 90% off the token price; nothing about your prompts changes.
18,412 of 21,006 tokens repeated across these runs. Same tokens, a tenth of the bill.
03
A model you own
The end state: a model fine-tuned on your traffic, yours, reached through the same endpoint as everything else.
It is trained three ways: distillation from a frontier teacher into a small model you own, reinforcement learning scored against verified outcomes in the simulation, and supervised fine-tuning on gold responses mined from your traffic.
Proved before it serves, and entirely opt-in. You turn it on; it never happens on its own.
01Your agent traffic
02We fine-tune a model on it
03Proved in a simulation of your company
04Deployed behind the same endpoint, as your model
One model is usually served by more than one deployment: the provider's own API, a cloud host, a pooled account, your own keys. They differ in price and nothing else that matters.
The gateway sends each request to the cheapest healthy deployment that can serve it, at provider price. Set the preference per team or per key: quality, speed, or cost.
Same model, three healthy routes: the cheapest deployment serves it, at provider price.
05
Load balancing
One provider's rate limit is not your ceiling. Traffic spreads across providers, regions, and keys, and pooled accounts absorb bursts on capacity errors before a request ever waits.
Nothing to configure per app: the waterfall is gateway policy, and the same model answers on every route.
Bursts spill across providers and pooled accounts on capacity errors, so throughput scales past any single key.
06
Prompt compression
Long prompts carry dead weight: boilerplate, stale context, examples the model stopped needing. You pay for every token of it, on every call.
We find the prompts that compress and show the diff. You apply it, send fewer tokens for the same result, and the saving repeats on every request.
We find the prompts that compress and show the token diff; the answer does not change.
From traces to a proved model.
01
Your traces
Connect Arize, Braintrust, LangChain, or your database. Or just route through the gateway: traffic is traces.
02
A simulation of your company
From the traces we build a simulation: a digital twin of production, with your tools, your data shapes, and your failure modes.
03
Scenarios: generated, executed, verified
The simulation generates scenarios, candidate models execute them, and every outcome is verified against it. That is where routing decisions and cache boundaries come from.
04
Training and proof
Your model improves against the simulation and has to win on held-out scenarios before it serves a single request.