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MVP · ARTIFICIAL INTELLIGENCE · VIBE CODING

AI shortens the path to an MVP. It does not skip the decisions.

AI accelerates research, interface, code and content. What decides the outcome is still the hypothesis you pick, the scope you approve and the human review behind every delivery.

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Without a clear hypothesis, AI only builds the wrong thing faster.

It starts with the hypothesis, not the tool.

Before anything is generated, we define the statement to be tested, who takes part in the test and which signal would count as evidence. That statement drives scope, interface and metrics — and keeps the build small enough to learn from.

Scope is what protects the budget.

  • One core journey, not six.
  • One audience, not every possible persona.
  • Integrations only when the test depends on them.
  • Real content from the first screen.
  • Metrics defined before any code is written.

Vibe coding, with judgement.

Vibe coding means driving the build in conversation with AI while reviewing every step. It works when there is product direction, a code standard and someone accountable for saying no. Without that, you get a prototype nobody can maintain.

MVP types AI genuinely accelerates.

  • Validation landing page with capture and analytics.
  • Interactive prototype for user testing and pitching.
  • Web application with authentication and a database.
  • Dashboard or internal tool replacing a spreadsheet.
  • Support or triage workflow with AI assistance.

How the work runs

  1. 1. Statement and scope

    Hypothesis, audience, validation criteria and an explicit list of what stays out of this version.

    • core hypothesis
    • essential journey
    • success criteria
    • approved scope

  2. 2. Assisted build

    Interface, code, data and content produced with AI support and reviewed at every delivery.

    • interface and identity
    • reviewed code
    • data and authentication
    • real content
    • analytics

  3. 3. Test and read the signals

    The MVP goes live for the right audience and the signals turn into a decision about the next cycle.

    • launch
    • instrumentation
    • user interviews
    • prioritised next cycle

How it is engaged

Through the existing formats: Applied Diagnosis for direction, Digital Sprint or a coordinated project for the build, Implementation Mentorship for evolution with your team.

See the engagement formats

What AI does not solve

It does not pick the right problem, validate demand, replace human review or guarantee users and revenue. It also cannot turn an undefined scope into a product — that decision stays with people.

Frequently asked questions

Is an AI-built MVP cheaper?

It is usually faster, which lowers cost in some stages. Price still depends on scope, integrations and finish level rather than on the use of AI itself.

Is AI-generated code reliable?

Only after review. Every piece of code goes through human reading, project standards and testing before launch. AI writes fast drafts; accountability for the delivery is ours.

Can the MVP evolve afterwards?

Yes. The architecture is chosen with the next cycle in mind, so database, authentication and content keep serving the product as it grows.

Do you implement complex bots and integrations?

Only inside the approved scope. Broad integrations, legacy systems and critical operations require their own project and are assessed case by case.

Keep exploring

Which hypothesis does your product need to test first?

Describe the idea, the audience and what has to be discovered. The initial reading points to a diagnosis, a prototype or a full MVP.

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