Scaling Simplicity
AI-assisted engineering that unlocks your organization's potential
For over a decade, we've built high-quality, scalable, and resilient software systems for the world's largest companies, without fail.
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 results.
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.
Our Approach
We use AI to build software and we build systems that use AI. Both demand deep engineering skill applied with discipline. AI tooling rewards teams who already know what good software looks like. We bring that knowledge, grounded in four principles: simplicity, evidence, judgement and rigour.
These principles shape everything we deliver, resulting in:
Rapid development of large IT systems
Increased levels of agility and software re-use
Lower maintenance costs
Secure, reliable systems that are robust yet flexible
Simplicity
The central problem in software engineering is accidental complexity, the unnecessary kind that accumulates when concerns that should be separate get tangled together. These tangles resist every future change.
AI-assisted development produces code fast, but speed compounds whatever structure is already in place. If the foundations are simple, that compounds well. If they aren't, you get a bigger mess, sooner. Simplicity isn't an aesthetic preference. It's an engineering discipline, and a hard-won one.
Evidence
Every team is different, and every individual within a team is different. What works in one context won't work in another, and the only way to know is to measure. We track the things that matter: velocity and quality.
We also track the things that matter to you, and report on them honestly, to demonstrate that we're adding value, to understand where to coach, where to intervene and where to get out of the way.
Judgement
AI makes code cheaper to produce, which shifts the bottleneck from writing software to evaluating it. Cognitive offloading is useful, but only up to the point where engineers still understand what's been offloaded. Past that point, you're accumulating debt you can't see.
Keeping human judgement sharp at the moments that matter, architecture, design trade-offs, the decisions that tools can't make, is what separates teams that move fast from teams that just produce a lot.
Rigour
Software that degrades the experience for customers, frustrates the developers maintaining it or falls over under load is just a liability shipped sooner. Velocity only counts when the thing being delivered works. For users. For the team. For the business that depends on it.
Operational soundness, user experience and reliability aren't extras you bolt on when there's time. They're the baseline. Without them, nothing else you ship matters.
