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The way most teams build physical AI today is a patchwork: one tool for simulation, another for the training loop, a hand-rolled data pipeline in between, and a fragile script to get onto the robot. Every seam is a place where formats break, assumptions drift, and weeks disappear. Cadenza replaces that patchwork with one stack, designed end to end — the SDK as the foundation you build on, and the CLI as the workflow that turns it into data, training, and deployments.

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The status quo vs Cadenza

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Where the advantage comes from

One coherent stack

Every layer shares the same physics core, action library, and policy interface — no glue code, no format conversions between stages.

The loop you test is the loop you ship

Simulation and hardware run the identical control loop, so sim-to-real transfer isn’t a separate, fragile rewrite.

Data that labels itself

Every mission is logged tick-by-tick and scored automatically, so training data arrives clean and labeled — not as a separate annotation project.

Fine-tuning without the boilerplate

LoRA-based fine-tuning keeps specialization cheap, fast, and composable — swap task-specific adapters on a shared base model.

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Why it matters: iteration speed

The point of a unified stack is velocity. Because every layer shares the same core, a change to a scene, a reward, or a policy flows through simulation, data, and fine-tuning without re-plumbing anything. Faster loops mean more rollouts, more data, and better policies — which is the whole game in physical AI.

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Start building

Build with the SDK

The foundation — simulate robots, build scenes, and run policies in Python.

Operate with the CLI

The workflow on top — gather data, fine-tune, and deploy.