Where it stalls
Everyone has the tools.
Almost nobody ships faster.
In 90+ tech due diligences, the pattern is the same: AI made individual developers faster, and nothing changed about how work gets specified, reviewed and shipped.
Vague tickets
A spec that only exists in someone's head makes agents write the wrong thing, faster.
No shared playbook
Everyone prompts their own way. No shared skills or conventions, so nothing compounds and agents have nothing to stand on.
Trapped knowledge
Product truth sits in Notion pages, Slack threads and two people. Agents cannot read any of it.
Demos, not products
AI MVPs ship in weeks, then stall: no tests, no architecture, nobody who understands the code.
Every company calls itself AI-native. Most mean their developers have Copilot or Claude Code open. AI-enabled is your team working with AI. AI-native is the system doing the work autonomously, which is the difference between a few percent faster and a multiple.
What we do
We launch AI-native products.
And we make companies AI-native.
It's one factory either way: the same agents, the same product brain, the same engineering discipline. The only question is whether we run it for you, or hand you the keys.
We build it for you
AI Product Studio
We design the architecture, set up the factory and ship your full product with agents, under senior engineering. You get production software, plus the code, the agents and the product brain.
Fixed price for the first phase, then per milestone.
Build my product →We install it in your team
AI Transformation
The same factory, connected to your repo, Linear, Slack, even Telegram. Your team gives the agents work the way they already talk to each other, and reviews what comes back.
Assessment €4–6K, credited against the 8-week Sprint it scopes and prices. Then €2–5K a month to keep the factory running.
Make my team AI-native →What's inside the factory: four agents and one brain
Product agent
Turns ideas into tickets with acceptance criteria and test steps.
Design agent
Takes the Product agent's output - a feature with its acceptance criteria - and builds an interactive prototype from your design system. You click it before anyone writes code.
Coding agent
Picks up “ready” tickets and opens a pull request for review.
Knowledge agent
Your way in and out of the product brain: it asks you to approve what goes in, and it answers your questions about what the product already knows.
Architecture, business logic, security and the final merge stay human. Everywhere else the agents move without waiting for anyone. Misapplied autonomy is what kills these projects, so the guardrails are real engineering: test-driven development, architecture decision records, CI/CD and review by senior engineers. Everything lives in your repo, runs on your own AI subscription, and stays yours if we leave.