AI Engineering
AI-assisted development is moving from experimentation to enterprise adoption. We help engineering teams introduce it with rigour, measure its impact with credible metrics, and scale it to deliver concrete outcomes.
Enablement
We embed with your engineering team to introduce AI-assisted development into existing workflows. This is not a training exercise - we write code alongside your engineers and establish practices through working software.
At the same time, we stand up a measurement framework that gives leadership an honest picture of impact:
- DORA metrics as baseline indicators of delivery performance
- Code health metrics, including defect density and test quality measured through mutation testing
- AI contribution analysis, tracking AI-assisted versus manual contributions with quality indicators for each
The deliverable is a working measurement framework and an engineering team using AI-assisted development with confidence.
Acceleration
With the foundation in place, we apply AI-assisted development to deliver against your roadmap - faster.
We work with you to identify target milestones and use the measurement framework to demonstrate acceleration: quantifiable improvements in delivery speed, with quality held constant or improved.
During this phase, we gather sufficient data to identify where AI-assisted development delivers genuine productivity gains and where it introduces risk. This evidence base informs decisions about where and how to scale AI adoption further.
The deliverable is measurable acceleration against roadmap targets, validated by real data.
Transformation
Once confidence in AI-assisted development is established, we apply it to larger transformation objectives:
- Vendor replacement - moving vendor-supplied systems in-house, where AI-assisted development can dramatically reduce the cost and timeline of building replacements
- Legacy modernisation - porting legacy applications to a modern, cleaner platform, with AI accelerating the translation of business logic while engineers focus on architectural improvement
Each phase delivers standalone value. You can stop after any phase if the results do not justify continuing.
Governance
As AI-assisted development scales across your organisation, governance ensures it remains effective, safe, and aligned with business objectives:
- Policy and standards - establishing clear guidelines for when and how AI tools are used, ensuring consistency across teams and projects
- Quality assurance - continuous monitoring of AI-generated output against your engineering standards, catching degradation before it reaches production
- Risk management - identifying and mitigating risks specific to AI-assisted development, from intellectual property concerns to supply chain dependencies
Governance is not about slowing teams down. It is about giving leadership confidence that AI adoption is controlled, measurable, and delivering genuine value.
