Built in-house, not switched on from a vendor
Designed and built the AI capability that runs across our software development lifecycle. Not a licence we activated: an architecture I designed, with the scoping, guardrails and enablement that decide whether AI in engineering is leverage or liability.
AI Enablement, 2024-Present, nimbus Cloud
AI tooling arrives the way most new tooling arrives, which is unevenly. Some engineers get real leverage, others get confidently wrong code, and without a deliberate position nobody can say whether the net effect is positive.
Buying a product does not answer the questions that actually matter. What code review means when a machine wrote the change. What is acceptable to send to a third-party model when your customers include government and healthcare. How you avoid slowly eroding standards that took years to raise. Those are architecture and policy decisions, not procurement ones, which is why I built rather than bought.
Scoping first, rather than assuming AI helps everywhere. Test generation, boilerplate and migration work are strong candidates. Novel domain logic is where the failure modes are expensive and subtle, and saying so explicitly matters more than any tooling choice.
Then the guardrails, designed into the system rather than written in a policy document nobody reads: a clear position on what may be sent to which model, human accountability for every merged change regardless of what produced it, and pipeline checks that do not care whether a person or a model wrote the diff.
Enablement is the part most organisations underestimate. Working sessions rather than a link in a channel, shared conventions for our own codebase, and explicit permission to say a tool is not helping for a given task.
A working capability with a defined position on where AI belongs in the lifecycle, and guardrails enforced by the pipeline rather than assumed.
This is current work and still developing. Architecture diagrams and measured results are being prepared and will be added here.
AI Architecture, AI Governance, Developer Experience, Test Generation, Code Review, Usage Policy