RecursiveDB is the engine underneath Allocate: it captures every task your agents complete, binds it to the outcome, and turns the result into training signal. Rented models stay static. Yours compounds.
Signals stream in from real work, bind to outcomes, and land in the next training run. No labeling projects, no exports.
Because it is one: the trajectory store is the source of truth your models are trained from, not observability exhaust.
Every trajectory is recorded losslessly, so it round-trips into a training example with zero transformation loss.
A cleared dispute, a confirmed booking, an analyst's agreement: rewards are real outcomes bound to real work, append-only.
Model, prompt, tools, and sandbox image carry an immutable policy version, so every improvement is attributable and reversible.
Your signals train your model and nobody else's. The dataset never crosses your boundary, and the weights it produces belong to you.
Book a build session: we scope a live agent, connect one environment, and forecast your usage, in under an hour.
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