Point your agent at one endpoint. Ravelyn profiles every trajectory, finds the call shapes that repeat, and compiles those hot paths down to small models trained on your own traffic. The genuinely hard steps keep running on the frontier models you already pay for. Everything else gets cheaper every week.
No contract to see a profile. One week of traces is enough.
Example trajectory. Figures describe a representative support workload, not a guarantee.
A single agent task is not one model call. It is a dependency graph of a few hundred, and the graph is lopsided. A handful of steps are genuinely hard: decomposing an ambiguous request, composing an answer, judging whether a claim holds.
The rest is machinery. Picking a tool. Rewriting a query. Reranking six candidates. Coercing output into a schema. Scoring a draft. These are the same shapes, with the same structure, thousands of times a day, and almost every team routes them to the largest model in the account because that is the safe default and nobody has time to prove otherwise.
Ravelyn proves otherwise, continuously, on your traffic.
Nothing in production changes until you have seen the counterfactual and approved it.
Swap one base URL. The endpoint is OpenAI-compatible, so your SDK, your prompts and your provider keys stay exactly as they are. You get tracing, failover and per-shape cost attribution from the first request.
This stage is free and stays free. If you never go further, you have a better router than the one you had.
Wire your own success signal: the tests passed, the diff was accepted, the ticket stayed closed, the agent finished without a retry. Ravelyn treats that signal as the objective function.
This is the part that makes the rest work. A compiler with no notion of correctness optimizes for price and quietly ships worse answers. Yours optimizes against the outcome your product already measures.
Set a budget and a service level for the whole task, not for each request. Resolve the ticket under forty cents, under ninety seconds, above a 93% resolution rate. Ravelyn allocates model choice across the entire trajectory so a cheap decision at step three does not cost you forty calls at step forty.
Every plan runs in shadow against live traffic before it is eligible for rollout. You see the measured difference in cost, latency and outcome, side by side, and you decide.
Hot call shapes get a specialist model, trained on your own traffic and your own labeled outcomes, built on permissively licensed open-weight foundations, and served on our infrastructure at single-digit millisecond overhead.
Rollout is per shape and gradual, behind the guards below. One shape at a time, each with a measured baseline you can point at in a board meeting.
Borrowed directly from how a tracing JIT works. Optimize the common case, keep a guard on every assumption, and fall back the instant one breaks.
Each compiled shape carries a threshold you set. Below it, the request is handed straight back to the frontier model before a token reaches your user.
Your prompts change, your product changes, your users change. When the live distribution moves away from what a specialist was trained on, that shape deoptimizes automatically and queues for retraining.
A sampled slice of compiled traffic keeps running against the frontier path in the background, so the quality comparison is continuous rather than a one-off pre-launch test.
Revert one call shape without touching the other eleven. Rollback is a single API call and takes effect on the next request.
One flag returns 100% of traffic to your original providers and keys. Your integration is unchanged, so there is nothing to migrate back.
Not a markup on tokens. Not a seat. Not a platform fee.
of verified savings, measured against a baseline frozen and agreed in writing before the first optimization ships. If we save you nothing, the invoice is zero.
The whole model depends on access to your traffic, so the terms around it are the product, not the fine print.
Nothing is pooled. There is no shared model, no cross-customer training, and no case in which one account's data improves another account's results.
Specialists are built on permissively licensed open-weight base models, chosen per shape for the size and latency the workload actually needs.
Every specialist Ravelyn builds for you is yours. Export the checkpoints whenever you want and run them on your own infrastructure or anyone else's.
You keep your own accounts, your own keys, your own commercial terms and your own rate limits. Ravelyn calls them on your behalf and never resells capacity.
Choose trace retention from zero to ninety days, with field-level redaction before anything is written. Zero retention disables compilation and keeps routing.
For regulated workloads, the profiler and the compiler run in your VPC and traces never leave it. In private preview.
The economics need volume and repetition. We would rather say so now than in month three.
One base URL. The API is OpenAI-compatible, so your SDK calls, streaming, tool definitions and retries all behave the way they do today.
You set the floor and the guards enforce it. A compiled shape that cannot clear your confidence threshold hands the request back to the frontier model before anything is returned. If your outcome metric falls below baseline, the savings stop counting, which means our revenue stops too.
Drift detection catches it, the affected shape deoptimizes back to the frontier path, and it queues for retraining against the new distribution. You will see it in the guard log before you see it in your metrics.
It varies more than any vendor will admit. Extraction, classification, schema coercion, reranking and tool selection compile well. Open-ended planning and long-context reasoning mostly do not. Send us a week of traces and the profile will tell you your number rather than an industry average.
Routing is the first stage and the least interesting one. A router picks between models that already exist. A compiler builds the cheap path that did not exist before, using what the routing layer learned about your traffic.
Export your weights and your traces, point the base URL back at your providers, and you are done. There is no migration, because your original integration was never removed.
You get back a profile: your call-shape distribution, your compilable share, the modelled saving against your current spend, and the shapes we would compile first. No commitment, and you keep the analysis either way.