Skip to main content
Pavo finds the highest-value change to your production ML/DS surfaces, proves it against a real benchmark, and ships it. You can run that loop yourself, or have us run it with you. The two are distinct and complementary: same platform underneath, same modules, same data isolation. The only difference is who drives.

​
Self-serve

Designed for teams with data-science bandwidth who want to own and run the improvement loop themselves. Your team uses the Pavo platform to build system knowledge, generate and test approaches in parallel, and ship what moves the metric.

​
Forward-Deployed Scientist

Designed for teams who want a metric win proven before they staff it. Everything in self-serve, plus a Pavo scientist who embeds with your team and runs the loop with you until a metric moves, then hands it back so your team runs it.

Self-serve

Your team runs the loop on the Pavo platform.

Forward-Deployed Scientist

A Pavo scientist runs the loop with you, then hands it back.

​
Shared foundation

Both modes run on the same platform and the same two modules, System Knowledge and Agent Studio. Both keep your code, data, and learnings inside your own environment: a per-client Google service account, SOC-II/ISO-compliant, deployed in your cloud or ours. Nothing you choose here changes your security posture.

​
Side by side

Not sure which fits? Start a conversation, we’ll help you pick.