Question 1

Do I need my data stack set up before using Pavo?

Accepted Answer

No. You don’t need to rebuild your entire stack before you start! Bring one growth goal, such as improving user activation or repeat engagement, and connect the sources relevant to it. Pavo uses your data, code and experiments to understand how that part of your product works. Your team reviews the findings and approves the first plan. You can then add more sources and goals as you go, with each project building on what you learn.

Question 2

How easy is it to set up Pavo?

Accepted Answer

Start with a goal, connect the relevant sources, review Pavo’s plan, then approve your first project.

Question 3

Does Pavo replace our product, marketing, or testing tools?

Accepted Answer

No need to replace them! Pavo connects to warehouses, code and experimentation tools such as Snowflake, GitHub, Statsig and Eppo. It brings context from your existing sources into the improvement process, helping agents investigate opportunities and prepare changes for review. Your team keeps using established tools to run your app and measure experiments.

Question 4

How much data do we need to make Pavo work?

Accepted Answer

The right amount depends on your main goal, the data available and the effect you want to detect. Start by checking whether your data can support the decision, then agree how much evidence a live experiment needs.

Question 5

We already track conversions and user behaviour in our connected tools. Do we need to rebuild our data setup to use Pavo?

Accepted Answer

Start with your existing sources. Pavo can use connected warehouses, event history and uploaded exports, so your first step is checking access and data coverage.

Question 6

How do we connect data with Pavo?

Accepted Answer

You can connect your warehouse, product code, dashboards and experiment history, or start with uploaded files. Access is read-only and scoped to what your team grants. Don’t need every source on day one? Start with the data relevant to your main goal and add more later.

Question 7

How do we check that a change is ready for a live user test?

Accepted Answer

Start with the evidence. Pavo evaluates candidates offline against your agreed metric, cohorts and guardrails, so your team can review the strongest approaches before using live traffic. You’ll see what was tested, what held up and where the result has limits. Your team then approves a controlled live experiment suited to your user journeys and marketplace dynamics. Offline results identify promising candidates; live tests measure real user impact.

Question 8

Does Pavo need fixed user segments?

Accepted Answer

No. Start with your current data. Pavo can help you investigate which user signals matter for your goals.

Question 9

Does Pavo focus only on more clicks?

Accepted Answer

No. You set a business goal. Pavo can evaluate activation, retention, long-term customer value and incremental conversions alongside engagement. Your team agrees the metric and guardrails before agents explore improvements.

Question 10

How does Pavo evolve?

Accepted Answer

Pavo keeps accepted findings, evidence, and decisions about your system. Each new project starts with that knowledge, so agents can build on what your team has learned instead of repeating earlier work each time.

Question 11

Is Pavo simply an A/B platform?

Accepted Answer

There’s more to it! Pavo helps do the work that makes an experiment worth running: understanding the system, finding opportunities, building candidates and evaluating them offline. Agents explore approaches in parallel and prepare changes for your team to review. You still use controlled tests to measure live impact. Pavo then keeps the findings, so the next project builds on what you already know.

Question 12

We already have a great data science team. Why would we use Pavo?

Accepted Answer

Pavo gives your team more capacity to explore. Agents handle analysis, candidate development and evaluation in parallel, while your team sets the scientific direction and reviews the evidence. Your scientists stay in charge of the decisions that matter.

Question 13

We do not have a data science team. Can we still benefit from Pavo?

Accepted Answer

Yes! A Pavo applied scientist can work alongside your team to frame the goal, run an improvement loop and review the results. You can build your own capability as you go.

Question 14

If Pavo runs our experiments, will we lose control?

Accepted Answer

Absolutely not! You approve the plan and decide at review checkpoints. You can redirect agents, add work or change the approach. Pavo pauses before any external actions or production changes.

Question 15

Can we still launch campaigns and make changes when Pavo runs?

Accepted Answer

Of course! Your team still uses its existing tools to run campaigns and manage your app. If a change affects the question Pavo is working on, share it with the agents so the plan and evidence can reflect the new product context.

Question 16

Can Pavo make and test improvements, or does it just recommend changes?

Accepted Answer

Yes! Pavo’s agents can create candidate models, develop code, evaluate approaches offline and prepare PRs for review. Your team checks the evidence and approves what moves into production.

Question 17

Can Pavo help us improve activation, repeat engagement and user value as well as user acquisition?

Accepted Answer

Yes, across your user journey! Pavo can help investigate acquisition quality, activation, discovery, personalized notifications, marketplace matching and targeted incentives. Start with your goal, agree how you’ll measure it, then test the approach with the strongest evidence.

Question 18

Why should we trust Pavo’s results and technology?

Accepted Answer

You should be able to check the work! Pavo’s results show the sources they came from, how they were validated and their limits. You can inspect the code, analyses and evidence, then accept or redirect the work at checkpoints. Predictions and offline results stay separate from claims about live impact, which need controlled experiments. Your team decides what is ready to ship.

Continuously improve acquisition, discovery, repeat engagement and margins with agentic experimentation.

Dinner near you

Turn user signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what makes users choose you

Customer spotlight

Keep improvingwith the tools you already use

From growth question to validated change, Pavo runs the improvement loop.

Understand and plan

Build and test

Review

Learn and repeat

Deploy with confidence,on your terms

Your Questions,Answered

Continuously improve acquisition, discovery, repeat engagement and margins with agentic experimentation.Continuously improveacquisitiondiscoveryrepeat engagementmargins withagentic experimentation.

Turn user signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what makes users choose you

Acquire valuable users

Activate new users

Improve app discovery

Earn repeat engagement

Improve marketplace matches

Make incentives profitable

Customer spotlight

Keep improvingwith the tools you already use

Snowflake

Databricks

Statsig

GitHub

Eppo

Google BigQuery