KR&A
AI is the primary implementer, not an autocomplete. When the machine writes the code, the code stops being the record of what the business decided. We keep people on the decisions and the knowledge somewhere people can still read it.
Sovereignty is architectural, not contractual. A European region on a hyperscaler invoice is not control. We build on open source and open weights wherever possible, and hold the remainder to jurisdictional guarantees you can enforce.
Every system encodes a distribution of value, whether anyone chose it or not. We make the choice explicit. Who it is built for, who is paid for it, and who is able to use it.
Three convictions, one practice. We do not run them as separate service lines, because our clients do not meet them as separate problems. A system built faster than it can be specified, running on infrastructure you cannot leave, trained on data nobody can account for, is one failure with three names.
We work in four modes across all three. We assess, so you know where you stand before anyone commits to anything. We train, because a method that does not transfer is just another dependency. We build, alongside your engineers rather than instead of them. We govern, so the position still holds after we have gone.
Where your engineering organisation actually stands. Where decisions are made, what gets written down, which reviews are real, and how much AI-assisted work survives to production.
For business critical systems, the question is not how much a machine can write. It is where a person has to decide, review and sign. We put those control points in the architecture rather than leaving them to individual discipline.
Generated code is disposable. What the business agreed is not. We hold that agreement in behaviour specifications, in interface contracts written before implementation, and in tests that read as statements of intent, so the organisation still knows what it built and why.
Practitioner training and co-delivery. Your team is leading the work by the end of it.
Every component of your toolchain measured against one test. Forkable under an open licence, and jurisdictional control of data flow at runtime. Vendor domicile does not pass it.
A working European stack. Git forge, project management, communications, models and orchestration. We run our own business on it.
Off proprietary platforms, with the exit cost established before you commit rather than discovered when you leave.
An unannounced vendor model update is a re-certification event. With a proprietary model, your compliance posture is set by someone else. Open weights return that decision to you.
The European Accessibility Act has applied since June 2025 and is now being enforced. Most digital estates fail it. We find where, fix what matters, and leave the standard in your definition of done rather than in a remediation backlog.
Where the material came from, who is owed for it, and how the system proves both. The Distributed Equity Licence applied in practice.
An AI-native delivery method changes the shape of an engineering organisation. Which roles move, which disappear, and what a fair transition looks like. Nobody selling the method wants to answer this. We put it in the scope.
Chapter I, universal scope, in force since February 2025. It applies whatever your risk classification, which makes it the first obligation rather than the last. We assess the position and deliver the training that discharges it, at practitioner level rather than awareness level.
An assessment is the cheapest way for both of us to find out whether we are the right people for the work.
Three to five weeks. A baseline you can act on, and no commitment beyond it.
Book a Discovery Call90% of technology professionals now use AI at work. Most organisations have invested in licences. But DORA’s 2025 research — based on nearly 5,000 professionals — reveals a critical truth: AI adoption alone has only a modest impact on performance. Without the right foundations, you’re amplifying dysfunction, not delivery.
AI is an amplifier. It magnifies the strengths of high-performing organisations and the dysfunctions of struggling ones. The greatest returns come not from the tools themselves, but from investing in the foundational systems that enable success.
— DORA State of AI-assisted Software Development, 2025The assessment evaluates your organisation against the seven foundational capabilities identified by DORA’s research as proven amplifiers of AI’s positive impact on performance.
Ambiguity creates risk. A clear policy provides the psychological safety needed for effective experimentation.
The benefits of AI are significantly amplified by high-quality, accessible, and unified internal data.
Connecting AI to your internal documentation and codebases moves it from a generic assistant to a specialised expert.
As AI increases the velocity of change, version control becomes the critical safety net that enables confident experimentation.
This discipline counteracts the risk of AI generating large, unstable changes, ensuring that speed translates to better product performance.
A focus on user needs ensures AI-accelerated teams are moving quickly in the right direction. Without it, AI adoption actively harms team performance.
A platform provides the automated, secure pathways that allow AI’s benefits to scale across the organisation.
DORA identifies the gap. SDD fills it. Specification quality is the shifted bottleneck in AI-native development — AI can only be as good as the spec it receives.
Four phases. Discrete deliverables at each stage. A clear narrative arc from diagnosis to action.
Capability survey across all seven DORA dimensions. Stakeholder interviews. Team skills self-assessment sessions. Map your team archetype.
Value stream mapping workshop. Identify where work is waiting, not working. Specification quality audit. Find the real bottlenecks.
Facilitated team workshop. Impact/effort mapping. Ruthless prioritisation. Commit to a first step with ownership and success criteria.
Define leading indicators (capability maturity) and lagging indicators (DORA outcomes). Establish baselines. Build the measurement playbook.
Every assessment produces a set of actionable deliverables. This is a roadmap, not a slide deck.
10–15 page report for leadership: current state, key findings, prioritised recommendations, and the business case for investment.
Visual representation of capability maturity across all seven dimensions, mapped to DORA team archetypes.
Current-state and future-state maps with process time, wait time, and flow efficiency for each step.
Team skills heatmap with identified capability gaps and recommended training priorities.
Maturity assessment of how work is specified and communicated to AI tools, with SDD-informed recommendations.
Impact/effort mapped initiatives with owners, success criteria, and a committed first step.
Leading and lagging indicators, collection methods, target cadence, ownership, and baseline values.
A follow-up review to evaluate progress on committed actions and recalibrate priorities.
You've invested in AI tooling. Adoption is patchy. You need evidence-based guidance on where to invest next and a framework to justify that investment to the board.
Your platform is the distribution layer for AI's benefits. This assessment shows where your platform is amplifying value — and where it's creating bottlenecks.
Your teams are using AI but results are inconsistent. You need a diagnostic that names the gaps and a workshop that gets your team aligned on what to fix first.
This assessment is grounded in the 2025 DORA AI Capabilities Model — the most comprehensive study of AI in software development to date. Every capability, every outcome measure, every team archetype is validated through rigorous research.