How kster.ai works
Set strategy. Discover solutions. Pressure-test them. Spec for the build. Every step is an AI-powered workflow on one shared context, and your AI coding agent works from that context across both discovery and delivery, not just the build.
Structure
The context that makes the AI know your product. Every workflow runs on top of it.
Start with a conversation.
No blank canvas. The AI interviews you in plain language and sets up your goals and the first problems worth solving. This is where the AI starts learning your product.
Build it layer by layer.
You give the context; the AI structures it. Each finished layer feeds the next and becomes the fuel the AI uses in every workflow. Goals to problems to ideas to tests to build-ready stories.
See the whole product at a glance.
One tree, from your business goal down to every idea and test. Click any branch and see why it exists and what it connects to.
Keep the people in view.
Who you're building for lives right next to what you're building. The AI's opportunities and solutions are always grounded in your actual personas.
Know if it's working.
Each goal carries its own number and target. Progress is a glance, not an end-of-quarter dig. The metrics root everything below them.
The work you run
You run each step. The AI does the labor inside it and checks its own output against reality before handing it to you.
Set strategy
Define outcomes that are worth chasing.
You brief the AI; it proposes real metrics with a leading-indicator test. If the Product Outcome moves but the Business Outcome doesn't, the link is wrong and the metric is rejected. Activity metrics don't pass.
Frame the real problems.
You surface the pain; the AI structures the opportunity and enforces the problem-space rule. Solution language in a problem statement gets caught and pushed back. Personas come from your product line, never invented.
Discover solutions
Brainstorm genuinely different approaches.
Research is mandatory before any option is named. The AI produces five distinct approaches (baseline, lateral, minimal, radical, AI-native), each connecting back to the opportunity's pain. Trade-offs are required. Five flavors of the same idea don't pass.
Pressure-test
Surface the risky assumptions.
The AI reads the solution and identifies what must be true for it to work, filtered to only the critical ones: the solution fails if false and you have little evidence today. Pass/fail criteria are set before any test runs. 'We'll learn something' is not a criterion.
Prototype the one moment that matters.
The AI builds a self-contained HTML prototype using your real design tokens (extracted from your codebase). One screen, scoped to the decision you need to test. No facilitator notes, so the result isn't contaminated. Scope is capped at the critical moment.
Spec for the build
Map the user journey.
The AI updates the story map where the feature belongs. A placement decision tree keeps the map honest. Every story passes an INVEST check: if it reads like a tutorial click-step, it's merged up.
Slice into build-ready stories.
Vertically-sliced, INVEST-compliant stories an AI or a team can build independently. A completeness check walks every constraint and edge case line by line so nothing is summarized away.
Write acceptance criteria.
Gherkin scenarios (happy path, alternates, edge cases, negative) plus the analytics events for your platform. Human Verification bullets set the minimum scenario coverage. This is the spec the AI builds against, and the gate the build is judged by.
Connect
Build with it and stay current. The spec hands off directly to your AI coding tools, and results flow back in.
Your AI tools plug in and build.
Claude Code, Cursor, Copilot: they read the acceptance criteria and solution spec before writing a line, and what they learn flows back in. The build is judged against the Gherkin scenarios written upstream.
It never goes stale.
Decisions and test results flow back into the picture so it stays current as reality moves. wip-briefing synthesizes the current state of work across the tree, so your team is always aligned on where things stand.
Grounded in your real stack.
codebase-detector scans your linked codebase and extracts your design system and analytics platform. This is what lets prototypes use your real design tokens and acceptance criteria name your real analytics events.
Start with one product line.
Free. No card needed.