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About · Methodology

From idea to operating system, without a black-box project.

Most AI projects do not fail because the technology is weak. They fail because the problem was never clearly framed. Our methodology forces clarity before build and control during rollout.

5

phases from discovery to optimisation

1-2 w.

typical range for discovery and architecture

4-8 w.

common build window to a productive core release

Weekly

demo and decision cadence instead of black-box delivery

Core principle

Get clarity on ROI, risk and scope before building.

We do not place an AI layer on top of a vague ambition. Each phase deliberately removes uncertainty and creates the next decision boundary.

  • First we identify whether the actual bottleneck is demand, conversion or internal execution.

  • Architecture and privacy choices are made concrete before implementation expands.

  • Rollout stays observable because demos, feedback loops and escalation rules are introduced early.

Sequence

The RakenAI methodology in five deliberate moves.

Depth varies by project, but the operating logic stays the same: sharpen the problem, shape the system, go live with a focused core and optimise from real usage.

  • 1
    Phase 1

    Discovery

    1-2 weeks

    We analyse the business model, funnel, systems and workflow friction to find the highest-value surface first.

    The output is not a generic idea list. It is a prioritised view of ROI, risk and technical effort.

    • is the issue demand, trust or operations?
    • which systems are involved?
    • which metric should move first?
  • 2
    Phase 2

    Architecture

    1-2 weeks

    Infrastructure, permissions, integrations and data paths are defined so the build does not later collide with compliance or process reality.

    In sensitive environments this phase determines whether the system will remain durable and expandable.

    • deployment model
    • roles and guardrails
    • API and data logic
  • 3
    Phase 3

    Development

    4-8 weeks

    Systems are built iteratively and tested against real use cases instead of disappearing into a long silent build cycle.

    Weekly demos keep scope tight and expose weak assumptions before they become expensive.

    • incremental build
    • early integrations
    • controlled testing
  • 4
    Phase 4

    Launch and training

    1-2 weeks

    Go-live means enablement, handoff logic and explicit escalation rules, not only technical activation.

    Teams adopt systems more reliably when they understand how the system behaves and where its limits sit.

    • training
    • documentation
    • human handoff logic
  • 5
    Phase 5

    Optimisation

    ongoing

    After launch we improve behaviour, response quality and process effect from real usage rather than abstract wish lists.

    We extend only where data and operations justify it instead of stacking features for their own sake.

    • monitoring
    • conversation tuning
    • prioritised expansion
What keeps the method stable

Three principles that keep projects fast and durable.

The method is not there to slow work down. It exists to protect speed from turning into rework.

ROI before novelty

We prioritise by operating value and economic leverage, not by how fashionable a capability sounds.

Control before convenience

Infrastructure, permissions and data paths are decided early so expensive rollback is avoided later.

Adoption in daily work

Systems need to make sense to the people using them, not just to the demo audience. That is why training and handoffs are designed from the start.

Self-hosted LLMs (Llama, Mistral, Phi)Swiss/EU DatacenterGDPR/DSG-compliant
Next step

Start with the right execution block instead of a project that is too large.

We can map which phase makes the most sense as the first move for your current environment.
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