Engineering
Modern software delivery: small teams, big leverage
The engineering practices and platform choices that let lean teams ship reliable software at enterprise pace.
Read article →The patterns in this article come from our work with large enterprises across regulated and fast-moving sectors. The aim is not to be exhaustive - it is to surface the handful of decisions we see making the biggest difference in practice.
1. The pilot trap
Almost every large enterprise we work with has run a generative AI pilot in the last 18 months. Far fewer have a single use case in production with measurable business value. The gap is rarely a model problem - it is a productisation problem.
2. Treat AI features as products, not experiments
A production AI feature needs the same things as any other product feature: an owner, a roadmap, observability, evaluation, a feedback loop, and a definition of done. Pilots that skip these steps tend to die in the gap between data science and engineering.
3. Evaluation is the new test suite
For deterministic systems we have unit tests. For probabilistic systems we need evaluation suites that capture quality, bias, latency and cost across realistic scenarios - and that run on every change. Without them, regression goes undetected until users notice.
4. Guardrails belong in the platform
Prompt injection, data leakage and hallucinated outputs are platform problems. Solving them once - in a shared AI gateway with logging, redaction and rate limiting - is dramatically cheaper than solving them in every team.
5. Pick boring use cases first
The fastest route to credibility is a boring, well-instrumented use case that saves real hours. Once that is in production, the appetite for ambitious use cases grows - and so does the trust to deliver them.
Where to start
If any of the above resonates with what you are working through, we are always happy to compare notes - without obligation. Email is the best way to reach us: customerservices@halfteck.com.