Turn real-world behaviour and expert decisions into controlled improvement loops, so AI keeps learning without drifting beyond the boundaries you set.
Improving AI for teams deploying in:
Four steps, one surface. The traffic that arrives at the gateway is the same traffic you compare, evaluate and hand to an agent.
Swap your base URL and every call through Stimulir is captured as a trace: request, response, latency and route. Filter by key, model, status or tag and see exactly what your product is doing.
Replay the same traffic through a second model and see answer quality and latency side by side before you switch anything. Comparisons run on your real inputs, not a benchmark.
Turn traces into a dataset, evaluate it in Lab with the judge and model you choose, and promote the prompt or model that wins. The loop closes on the same surface where the traffic arrived.
Hand a task to the Stimulir agent, or to your own coding agent through the CLI. It reads your traces, runs the evaluation and proposes the fix, with a person approving before anything ships.
npx stimulir setup
Mission
Walkthroughs that play step by step in the docs for the quickstart, API keys, datasets and the first evaluation. Watch it before you do it.
Pro tokens cover the listed models, so a team sends its first real request without adding a provider key.
An empty thread now opens with three starter cards: send your first real request, compare two models, run the demo.
Every failure on the real traffic path now says what happened and what to do next, with the code kept for support.
Pro for 7 days, no card and no provider key. About five minutes to a first trace. We are early, so the founder still takes every call.