A prototype that is already the product.
Stop validating on mockups nobody really uses. What you assemble has real accounts, data and journeys from day one: you test with real users this week, and you hand over code, not a PDF.
- single object, from prototype to shipped product
- 1single object, from prototype to shipped product
- designed interface blocks, to place rather than draw
- 81designed interface blocks, to place rather than draw
- mockups to redraw in code after validation
- 0mockups to redraw in code after validation
A mockup tests nothing. A real product, however small, tests everything.
A product team's classic cycle is long and fragile: you mock up, explain the mockup to developers, wait a sprint to test a single hypothesis, discover in development what the mockup was hiding (empty states, real data that doesn't fit, journeys that don't hold) and start over. Six weeks, often, between the validated idea and its first testable version.
Prototyping tools shortened part of the path, but a clickable prototype is still a guided tour: no accounts, no moving data, no usage analytics. What you observe in a user test is a reaction to a staging, not to a product. And the day the decision is made, everything has to be redrawn in code.
Kubbler removes the distance between prototype and product. You assemble from 81 designed and proven interface blocks (navigation, forms, tables, pricing, calendar) on a real data model, with real accounts and usage analytics included. The AI interprets your intent to compose and adjust; the foundation, written by engineers, carries accounts, data and security. What you validate is already readable code the technical team takes over as is.
Validate on a mockup, or on a product.
What a classic validation cycle costs, and what it becomes when the prototype is already real.
What a prototype needs to test anything.
A prototype without accounts or data tests nothing. Here both are there from the start, and what you validate is handed over as is.
Real from day one
What makes a user test a real test, and not a guided tour.
Accounts
Sign-up, login, roles: real users with real access, not personas.
Data
A real, versioned model, lists move, states change, edge cases appear.
Analytics
What testers do, screen by screen, from day one. To decide with numbers.
Handoff-ready
What saves you from redrawing everything once the decision is made.
81 blocks
Navigation, forms, tables, pricing, calendar, dashboard: assembled, not drawn.
Brand
Brand guidelines set once (colours, typography, themes) applied everywhere, no retouching.
Exportable code
What you validated goes as is to GitHub and to the developers.
What you can build with it.
Three situations where a mockup is no longer enough, and where a real product, however small, changes the decision.
A prototype on real data
Not frozen screens: accounts, moving lists, empty and full states, journeys you test with real users. What you observe is a reaction to the product, not to a demonstration.
A feature tested before it's decided
You place it, you measure it, you decide with numbers, before committing a team for a quarter. The cost of a hypothesis drops to one afternoon.
A product handed to developers with the code, not a mockup
What you validated is already readable, versioned code, with its data model and brand: the technical team starts from there, not from zero. No more mockup-to-code translation, and what it loses along the way.
How it works, concretely.
Four steps, three actors: you carry the product intent, the AI interprets it, and the engineers who wrote the foundation guarantee the prototype is already real.
You set the intent
The hypothesis to test, the journey, the data it handles, the users concerned. In natural language, or by importing an existing design: an HTML export becomes an editable page.
The AI interprets
It composes the screens from the 81 blocks, draws the data model, connects the journeys. You point at an element in the preview and say what changes: it adjusts live, within your brand guidelines.
The foundation carries
Real accounts, a versioned database, roles, usage analytics, security, hosting: written and operated by engineers. That is what makes a user test a real test, not a guided tour.
You test, measure, hand over
You invite real testers, read what they do screen by screen, decide with numbers. Then the technical team takes the code as is (validated, readable, versioned) and starts from there.
Why Kubbler, for a product team.
Four reasons that change the quality of decisions, not just the speed of deliverables.
You observe usage, not reactions
With real accounts, data and journeys, a user test produces facts: where people hesitate, what they abandon, what they come back for. Usage analytics are included. You decide on numbers, not on impressions from the room.
You assemble, you don't redraw
81 designed and proven interface blocks (consistent, accessible, responsive, in your brand) and 4 visual editors. You spend the time on the hypothesis and the journey, not on the pixel of a component someone already designed.
The handoff loses nothing
What you validate is already code: readable, conventional, versioned, with its data model and brand. The technical team starts from there, not from a PDF to interpret. The distance between what was tested and what will ship drops to zero.
The foundation is written by humans
The AI composes and adjusts your screens; accounts, data, security and hosting are written, reviewed and operated by engineers. Your prototype inherits a level of foundation even a mature product struggles to reach, and has nothing to hide at handoff.
The questions we get asked.
Precise answers, because decisions are made on facts, not on promises.
No: what you assemble is a real product, with accounts, a database and real journeys, live on a real domain. You test it with real users and real data, not with clickable screens, and what's validated is already code.
Readable, conventional code, exportable to GitHub: the versioned data model, the screens, the brand, the journeys. They start from what you validated, not from a PDF to interpret, and the foundation everything rests on is documented.
Yes: an HTML export (from Claude Design for instance) or an archive drops into the workshop and becomes an editable page, laid on the foundation. You start from your art direction, not from an imposed template.
Usage analytics are included from day one, GDPR-compliant: you see what users do on the journey you laid out, screen by screen, and you decide with numbers before committing a team.
Entirely. The brand identity (colours, typography, radii, light and dark themes) is set once in a dedicated editor and applies to the 81 blocks. You can also compose your own blocks and keep them in your library.
Yes: the workspace hosts your team with roles (product, design, technical) and each contributes in the editors or through the conversation. The Team plan includes access management for teams that deliver in series.
Test for real. Decide fast.
Free during the beta. The next decision is made on a running product, with real users, not on a frozen screen.