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The ape factory Method

From complex challenges to lasting impact.

A pragmatic, engineering-led approach to design, build and operate cloud, software and AI solutions that create lasting business value.

Discover. Design. Build. Verify. Operate.

Discuss your challenge
  • Business value first

    Start with outcomes, not buzzwords.

  • Engineering excellence

    Make decisions explicit and verify the result.

  • Collaborative by design

    Work with your team, in your environment.

  • Built for what’s next

    Use production insight to keep improving.

How we work

A clear path from idea to impact.

The stages provide a common structure without forcing every engagement into the same shape. We may enter at a different point or revisit earlier decisions as new evidence emerges.

01

Understand

Discover

Establish the business problem, current environment, constraints, risks, stakeholders and desired outcomes.

What we do

  • Understand the current state and operating context
  • Identify constraints, dependencies and risks
  • Clarify business and technical objectives
  • Define measurable success and acceptance criteria

What remains

  • Agreed problem and scope
  • Current-state understanding
  • Risks and constraints
  • Success criteria
02

Decide

Design

Turn what we learned into an actionable technical direction, balancing architecture, security, operations, cost and delivery risk.

What we do

  • Define target architecture and technical approach
  • Evaluate important trade-offs
  • Define delivery and operating models
  • Plan implementation and validation

What remains

  • Target architecture
  • Architecture decisions
  • Delivery plan
  • Verification approach
03

Implement

Build

Implement incrementally, using automation and short feedback loops to make progress visible and reduce delivery risk.

What we do

  • Implement infrastructure, platforms, software or data capabilities
  • Automate repeatable delivery and operational tasks
  • Integrate with the customer's environment and tooling
  • Review and refine continuously during implementation

What remains

  • Working solution
  • Automation and configuration
  • Technical documentation
  • Operational knowledge
04

Prove

Verify

Validate the solution against the technical and outcome criteria established earlier in the engagement.

What we do

  • Validate functional and technical behavior
  • Test security, resilience and operability where applicable
  • Review agreed acceptance and success criteria
  • Resolve gaps before operational handover

What remains

  • Verification evidence
  • Acceptance results
  • Production-readiness assessment
  • Known limitations and follow-up actions
05

Run & improve

Operate

Use operational insight to maintain the system, identify improvements and inform the next engineering decisions.

What we do

  • Observe health, performance and operational behavior
  • Maintain and improve delivered systems
  • Learn from incidents and changing requirements
  • Feed operational insight back into discovery and design

What remains

  • Operational insight
  • Improvement opportunities
  • Updated priorities
  • A sustainable operating model

What makes us different

Engineering depth. Real-world experience.

The ape factory Method connects business context, engineering decisions and operational reality. Each stage has a purpose, produces tangible outputs and creates the input for the next decision.

Outcome before implementation

We establish the problem, constraints and success criteria before committing to a solution.

Decisions made explicit

Architecture and delivery choices are documented so teams understand what was built and why.

Build where it belongs

We work in the customer's environment and tooling wherever appropriate, so systems and knowledge remain with the customer.

Evidence before assumption

Verification is a deliberate gate: we test technical readiness and the criteria agreed at the start.

Operations close the loop

Production insight feeds future improvements instead of ending when project delivery is complete.

Engagement models

Flexible ways to work together.

Our method describes how we deliver. These engagement formats define how we work together, from a focused assessment to ongoing operations. Scope, deliverables and responsibilities are agreed before work starts.

013–5 days

Expert Sprint

Assess & Blueprint

Clarify a technical challenge, review an existing system or define an architecture.

025–15 days

Engineering Sprint

Prove & Implement

Deliver a bounded technical implementation against agreed acceptance criteria.

03Agreed individually

Project-based

Build & Integrate

Deliver substantial applications and platforms through defined milestones and iterative engineering.

04Ongoing

Ongoing

Operate & Improve

Operate and improve supported environments under clearly agreed responsibilities.

Discuss your project

Engineering in practice

Evidence, ownership and continuous improvement.

The details matter. These principles connect delivery to the way your team will run, maintain and evolve the result.

A deliberate gateVerify what was agreed in Discover.

Discover and Verify are connected. At the beginning, we define what success means. Before calling the work complete, we return to those criteria and evaluate the result against them.

Evidence depends on the engagement and may include automated tests, technical validation, security checks, resilience testing, performance measurements, acceptance criteria or operational-readiness reviews.

Your environmentBuilt with you. Owned by you.

We prefer to work in the systems, repositories, cloud accounts and engineering workflows that belong to the customer where the engagement allows it. The goal is to strengthen your engineering capability, not create unnecessary dependency on ours.

Architecture decisions, automation, documentation and operational knowledge should remain usable by your team after delivery.

Engineering acceleratedAI accelerates the work. Engineering discipline governs the outcome.

We use automation and AI-assisted engineering where they improve speed, consistency or analysis. They do not replace architecture decisions, engineering review or verification.

  • Use automation for repeatable work
  • Use AI where it creates measurable leverage
  • Keep engineers accountable for technical decisions
  • Verify generated or automated outputs before relying on them
  • Keep important decisions and artifacts traceable
Continuous improvementOperate is not the end of the method.

Performance, incidents, usage patterns, cost and changing requirements create production evidence that becomes input to the next improvement cycle.

  1. Discover
  2. Design
  3. Build
  4. Verify
  5. Operate
  6. Discover

The method in practice

From strategy to real-world solutions.

Explore how the method applies to the platforms we build, the engineering expertise we bring and the systems we operate.

From discovery to a verified production solution.

Platform Engineering

Design, build, modernize and verify platforms and infrastructure.

Expertise where you need it.

Engineering on Demand

Bring us into the stage where targeted engineering expertise is needed while your team retains overall operational responsibility.

Operate, maintain and improve.

Managed Platforms

We take ongoing operational responsibility for an agreed part of your environment and use production insight to drive continuous improvement.

Frequent questions

Practical questions. Straight answers.

How do you typically start an engagement?
We start by understanding your problem, operating environment and desired outcome. Together we agree scope, constraints and success criteria before choosing the right engagement format.
Do we need to follow all five stages?
The stages provide a common structure, not a fixed project plan. We can join at the point where you need us and revisit earlier decisions as new evidence emerges. Verification remains tied to the outcomes agreed at the start.
Can you work with our existing team and tools?
Yes. Where the engagement allows it, we work in your repositories, cloud accounts and engineering workflows, alongside your team.
How do you ensure knowledge transfer?
Architecture decisions, automation, documentation and operational knowledge are part of delivery. We aim to leave your team with artifacts it can understand and continue to use.
How do you use AI in engineering?
We use AI and automation where they help with speed, consistency or analysis. Engineers remain accountable for decisions, review and verification of the outputs.