AP4 Digital
HU/EN
Book a professional consultation
Book a consultation
AI-native delivery · named professional decision gates · visible outcomes

A development process from the first decision to live operation.

AI is part of the entire delivery system; accountability is not delegated. At every stage, we define what machine assistance accelerates, what a named professional reviews, which tangible outcome the client sees and what the next decision gate is.

Discuss your project with us Product discovery

Brief professional discussion · goal, risk and next decision

AI-native delivery: acceleration with verifiable accountability

We use AI to support repetitive, comparable and testable work; the business goal, product priorities, architecture, critical code, security and release remain human responsibilities.

The goal is a shorter feedback cycle and more delivered value, not bypassing review or quality gates.

  1. Discovery

    What AI accelerates
    synthesis of interviews and documents, preparation of requirements contradictions, alternatives and risk questions
    Human gate
    The named business and product owners approve the goal, metric, priority and go/no-go decision.
    Client-visible outcome
    Target state, prioritised scope, risk list and decision recommendation.

  2. UX and prototype

    What AI accelerates
    preparation of flow variants, content states, research summaries and prototype details
    Human gate
    The product designer and product owner review clarity, accessibility and business fit; where justified, we validate with real users.
    Client-visible outcome
    Key user flow, testable prototype and documented learnings.

  3. Architecture and delivery plan

    What AI accelerates
    preparation of technical alternatives, dependencies, interface drafts, risk scenarios and documentation
    Human gate
    A named architect approves data, integration, security, operations and release decisions.
    Client-visible outcome
    Architecture and delivery plan, estimate, acceptance criteria and decision log.

  4. Development

    What AI accelerates
    code drafts, repetitive implementation, refactoring proposals, test-case creation and documentation
    Human gate
    Developer review, static and automated checks, and named professional approval of critical business logic and security decisions.
    Client-visible outcome
    Working increments tied to acceptance criteria and demonstrable in short cycles.

  5. QA and release

    What AI accelerates
    preparation of test-case variants, regression coverage, log analysis, defect triage and release notes
    Human gate
    QA, security and release owners decide on compliance, residual risk and production readiness.
    Client-visible outcome
    Test report, known limitations, migration and rollback plan, and release documentation.

  6. Measurement and continued development

    What AI accelerates
    organisation of analytics patterns, feedback, incidents and roadmap options
    Human gate
    The business and product owners select what to improve, scale, simplify or stop.
    Client-visible outcome
    Metrics report, learnings, prioritised roadmap and next investment decision.

Communication and change management

At the outset, we define the project's cadence, decision deadlines, owners, and rules for AI use and data processing.

When scope changes, we make the impact on time, cost, risk and dependencies visible; the commitment changes only through an approved decision.

5 questions
FAQ.
* about the process

Frequently asked questions about the process.

Something else? Email hello@ap4.hu and we will answer directly.

Depending on the project cadence, through regular demos and in a verifiable test environment. The aim is to surface material decisions before the end of the project.
We document the change's impact on time, cost, risk and dependencies. The commitment changes only through an approved decision.
The technical, operational, user and release documentation agreed in the contract, together with the source code and required access, under the handover terms.
No. AI-assisted output passes through the same architecture, code, QA, security and release gates required for the project. The approver and decision remain traceable.
At project and portfolio level, we measure time from approved task to release, accepted features, rework and post-release defects. A public comparative claim may be used only after a stable baseline and approved methodology exist.

A good process means fewer late surprises, not more ceremony.

Show us the decision context, and we will propose the right entry stage and accountability model.

Request a consultation

Brief professional discussion · goal, risk and next decision