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AI delivery and governance
AI-assisted development and governance
We support AI-assisted software development, from project scoping to systems integration. Architecture, code review, testing, security and data governance are part of the delivery process.
Our position
Quality controls for AI-assisted development
AI-assisted development increases code output. Review, testing and maintenance processes need to keep pace to control technical debt.
Without shared conventions, AI-generated contributions can diverge in architecture, naming and behaviour. We define project standards and checks to maintain consistency.
AI reliability depends on data quality. Input data and outputs reused by the system need controls, documentation and clear ownership.
What actually goes wrong
Four risks to manage in AI projects
- Divergence, within weeksDocumented architecture and development standards reduce inconsistencies in naming, structure and behaviour across generated components.
- Code in production nobody reviewedNamed reviewers validate generated code. Review records and test results are retained before production deployment.
- Security debt that arrives silentlyGenerated code pulls in libraries, and libraries carry vulnerabilities. Without scanning in the loop, a project inherits a dependency tree nobody chose and no one is watching. This is mechanical to solve and almost never set up at the start.
- The data degradation loopModel output becomes input. Without freshness, lineage and ownership on the data underneath, quality falls over successive iterations, and the decline is invisible until a business decision is visibly wrong.
From our own delivery
Project example: an operations console prototype
One of our senior consultants, embedded for years on the integration platform of a global container shipping group, rebuilt its entire operating console with AI assistance: setup management, versioning, quality control, packaging, graphical workflow editing and production monitoring. Frontend, backend, database procedures and APIs. Two months, one person.
He had spent years inside the platform's data model, its business rules and its failure modes. Asked whether an outside developer with the same tools could have done it, his answer was no: the tools were available to everyone, the context was not.
Every validated screen produced a rule, recorded in project documentation rather than left in a chat history. The model re-reads that documentation before touching the next screen, which is what keeps twenty screens looking like one product.
The prototype uses content-addressed versioning, immutable released objects and quality gates that block non-compliant packaging.
Static analysis, secret detection, dependency and coverage scanning in a container, the report fed back for remediation, and a second review from a clean context with no project history. A dependency CVE and a thread-safety flaw were found and fixed this way.
This system is a prototype under test, not a production deployment, and it has not yet been through the client's own security review. We describe it because of what it shows about method, not to claim a production reference. The client is not named.
How we help
Our AI development services
Our approach combines architecture, development methods, quality controls and governance to deliver maintainable, testable and documented solutions.
AI delivery framing
Conventions, naming, architecture rules and a definition of done, written as documentation the model is made to re-read rather than as a slide nobody opens. Plus the arbitration of what is delegated to a model and what is not.
- Conventions
- Architecture rules
- Definition of done
- Prompt and context discipline
Review and security of generated code
Static analysis, secret scanning, dependency and vulnerability tracking, coverage thresholds, and an adversarial second review run from a clean context. Set up as a pipeline, not as a one-off audit.
- SAST
- Secret scanning
- SCA and CVE tracking
- Adversarial review
Solution architecture and integration
The part that decides whether a prototype becomes a system: data model, APIs, packaging, environments, migration and the interfaces to what you already run. Our founding discipline, applied to AI-assisted builds.
- Data model
- API design
- Packaging and environments
- Legacy integration
Data governance for AI
Freshness, lineage, ownership and reference data on the flows an AI system reads, with stated service levels on data age and error rate. This is what stops output quality decaying over successive iterations.
- Freshness SLAs
- Lineage
- Reference data
- Quality monitoring
Compliance and sovereignty
Where the models run, where the data rests and transits, what an AI system may read, and the evidence a regulator or an internal audit will ask for. Under the AI Act, DORA and NIS2, with a European hosting option when the perimeter requires it.
- AI Act
- DORA and NIS2
- European hosting
- Audit evidence
Team enablement
We help engineers use AI, review its output and apply project conventions. Methods and documentation are transferred to your team.
- Pair delivery
- Method transfer
- Review practice
- Documentation
How we engage
A phased approach to your project
5 to 15 days. What your teams already build with AI, what reaches production, what is reviewed, and where the exposure sits. Ends with a costed plan.
Conventions, review pipeline and quality gates on one real perimeter, so the rules are tested against actual delivery rather than written in the abstract.
A bounded solution delivered with AI assistance, inside that frame, with the architecture and integration work that makes it maintainable.
Your engineers run the method without us. Conventions, pipeline and documentation stay with you.
Assess your AI development practices
The question is what happens to that code in eighteen months. A 5 to 15 day assessment tells you where you stand and what it will cost to put a frame around it.