DesignProduct Discovery

Decide before you build.

Our designers and engineers use AI to turn product assumptions into working prototypes faster. We test with real users, refine what works and decide what deserves further investment.

Talk to human

What we’re actually testing.

Demand
Will people go through with it? Not whether they like the screens. Whether they get from first contact to the action that makes this a business.
Variants
More than one version, side by side. One version is a preference. Two are a test, with a result you can show to whoever signs off.
Foundation
Something to continue from. Tokens and components in code, so whatever wins is the first screen of the product rather than a thing to be rebuilt.

How it works

From assumption to evidence.

Frame the assumptions
We write down what the decision rests on and the one metric that settles the biggest assumption.
If we can’t agree on that metric, discovery isn’t ready to start.
Build & iterate
Designers use AI to generate working interfaces, with engineers reviewing the code from the start.
We explore alternatives and connect data where it matters for the test.
Test & refine
Put prototypes in front of relevant users.
Use what we learn to revise the experience and test again, with AI accelerating implementation between rounds.
Decide
We come back with what the tests showed against the assumptions we started from, and a recommendation on what deserves further investment.
Deciding not to build is a full result, and the cheapest one you’ll ever buy.

What you get

Working software and an answer.

A working prototype.: Real data, real logic, your stack, in a state your engineers can keep building on.
Test results against the hypothesis.: What people did, not what they said they’d do, with the sample and the method written down.
Components that carry forward.: Tokens and reviewed components in code, ready to continue into MVP development if the decision is to build.
A documented decision.: The hypothesis, the metric, the result and the recommendation, in a form that survives the meeting.

Why Cleevio

Product experience. AI-driven execution.

We’ve built products for clients and invested in our own. That experience helps us identify which assumptions are worth testing first. Our designers and engineers use AI to build and revise prototypes together, shortening the path from an idea to user feedback. Engineers review the code throughout, so suitable components can carry forward into MVP development.

Our stack