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 business goal, such as improving account activation or payment acceptance, and connect the sources relevant to it. Pavo uses your data, code and experiments to understand how that part of your business 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 banking, analytics, or testing tools?

Accepted Answer

No. Pavo works alongside your existing banking, data, and experimentation systems. Connect sources such as Snowflake, Databricks, dbt, GitHub, Eppo, and Google BigQuery to give agents the context to investigate opportunities and prepare changes for review. Your team keeps its established systems for customer operations, daily payment processing, production decisions, and live experiments.

Question 4

How much data do we need to make Pavo work?

Accepted Answer

The amount depends on your goal, available historical data, and the effect you want to detect. For fraud projects, check label maturity and rare cases. Your team agrees what evidence a controlled experiment needs before testing changes.

Question 5

We already track transactions and customer behaviour in our existing systems. Do we need to rebuild our data setup to use Pavo?

Accepted Answer

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

Question 6

How do we connect data with Pavo?

Accepted Answer

Connect your warehouse, transaction records, code, and experiment history, or start with approved exports. Pavo’s source access is read-only and scoped to what your team grants. Begin with the data relevant to your goal, check coverage and freshness, and add more sources as your projects expand.

Question 7

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

Accepted Answer

Start with the evidence. Pavo evaluates competing candidates offline against your agreed success metrics, customer cohorts, and approved risk limits. For payment changes, review legitimate approvals alongside mature fraud losses and customer friction. Your team can inspect what was tested, which trade-offs emerged, and where the evidence has limits. You then approve a controlled live experiment to establish whether the change improves real outcomes within your agreed guardrails.

Question 8

Where is our financial data processed?

Accepted Answer

Pavo Cloud uses disclosed providers. Customer VPC uses your cloud account, with external processing enabled only when approved.

Question 9

Does Pavo focus only on payment approvals?

Accepted Answer

No. Your team defines success. Pavo evaluates legitimate approvals alongside mature fraud losses, customer friction, activation, and retention. You agree the metrics and risk limits before agents explore candidate improvements.

Question 10

How does Pavo evolve?

Accepted Answer

Pavo retains accepted findings, evidence, and decisions about your system. Each new project builds on that knowledge, helping agents investigate changing customer behavior and fraud patterns while learning from the work your team approves.

Question 11

Is Pavo simply an A/B platform?

Accepted Answer

Pavo helps with the work that makes an experiment worth running: understanding your system, identifying opportunities, building candidates, and evaluating them offline. Agents explore approaches in parallel and prepare changes for review. Your team uses controlled tests to measure business impact alongside customer outcomes and risk. Pavo retains the findings, so each new project builds on the evidence from earlier investigations and experiments.

Question 12

We already have an experienced data science team. Why would we use Pavo?

Accepted Answer

Pavo gives your team more capacity to explore. Agents develop candidates and run evaluations in parallel, while your scientists define objectives, apply risk limits, and review the evidence. Your team keeps responsibility for scientific judgment and production decisions.

Question 13

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

Accepted Answer

Yes. You can work with a Pavo applied scientist to frame the goal, run an improvement loop, and review results. Your team provides business context, accountable owners, and agreed risk limits.

Question 14

If Pavo runs our experiments, will we lose control?

Accepted Answer

No. You approve the plan and decide at review checkpoints. Redirect agents, add work, or change the approach. Pavo pauses before external actions and production changes requiring your team’s approval.

Question 15

Can we still update policies and make changes when Pavo runs?

Accepted Answer

Yes. Your team continues using its established systems for customer communications, payment operations, and product changes. Share updates that affect Pavo’s current project so agents can account for new policies, shifting data, and overlapping experiments when assessing evidence and proposing next steps.

Question 16

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

Accepted Answer

Pavo’s agents can build candidate models, develop code, evaluate approaches offline, and prepare pull requests for review. Your team inspects the evidence and approves changes before production deployment.

Question 17

Can Pavo help us improve activation, payment approvals and customer retention as well as customer acquisition?

Accepted Answer

Yes. Pavo can help investigate acquisition quality, account activation, legitimate payment approvals, digital fraud, retention, and support resolution. Start with one goal, agree the business metric and risk limits, then evaluate candidates before a controlled live experiment.

Question 18

Why should we trust Pavo’s results and technology?

Accepted Answer

Check the evidence yourself. Pavo’s results link to their sources, validation methods, and known limits. Your team can inspect code, analyses, and experiment findings, then accept or redirect work at checkpoints. Review business performance alongside customer outcomes and risk, and use controlled tests to establish live impact. Documented decisions and retained findings make the work easier to inspect, challenge, and build on.

Continuously improve activation, approval rates, retention and fraud detection with agentic experimentation.

Finish setting up

Turn financial signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what drives stronger financial performance

Keep improvingwith the tools you already use

From business 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 activation, approval rates, retention and fraud detection with agentic experimentation.Continuously improveactivationapproval ratesretentionfraud detection withagentic experimentation.

Turn financial signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what drives stronger financial performance

Acquire valuable customers

Activate more accounts

Approve legitimate payments

Detect emerging fraud

Retain active customers

Resolve support issues

Keep improvingwith the tools you already use

Snowflake

Databricks

dbt

GitHub

Eppo

Google BigQuery