Skip to main content
Pavo improves the production ML and AI systems your business runs on: a search ranker, a recommendation feed, a notification engine. It learns how one system actually works, finds the highest-value change, proves it against a real benchmark, and ships it. Then it does it again. Improving a production system takes two things that are hard to automate: a deep, verifiable understanding of how the system works, and the applied-science judgment to know what is worth trying. Pavo is built as two modules, one for each.

​
Two modules, one loop

System Knowledge

Builds a deep, verifiable understanding of one production system: a reviewed, cited knowledge base of how it works, which metrics matter, and where it breaks.

Agent Studio

Spends that understanding: a fleet of agents that investigates a real question, proves an answer against a benchmark, and hands back something you can ship.
The two form a loop. System Knowledge grounds every investigation Agent Studio runs, and Agent Studio writes what it learns back as findings, so the system gets smarter about itself with every program.

​
How Pavo works

1

Connect your stack

Point Pavo at your warehouses, code, dashboards, and experiments. Every connector is read-only and scoped to what you grant. See Connectors.
2

Build system knowledge

Pavo indexes your sources into typed, cited facts, distills them into a tribal book you review, and scores it against a knowledge bench. See System Knowledge.
3

Run a program in Agent Studio

Hand Pavo a real question. It plans the work, puts a fleet of agents on it, and proves an answer, pausing for your judgment at every checkpoint. See Agent Studio.
4

Ship what moves the metric

Accept the result, ship the change, and keep the finding. The next program starts from it, so the work compounds instead of evaporating.
Pavo runs inside your own single-tenant environment, with read-only access and no customer-data egress by default. See Security.

​
Explore the docs

Connectors

Step-by-step guides to connect your warehouses, code, dashboards, and experiments.

Engagement models

Run the improvement loop yourself, or have a Forward-Deployed Scientist run it with you.

Security

How Pavo is deployed, what runs, and what leaves your environment.

Book a demo

See Pavo run against your own production systems.