Standardised how an engineering org ships AI features, with every one instrumented for evaluation and production observability before release
We delivered an AI development lifecycle programme for a leading observability company's engineering teams, establishing a single governed path from prototype to production in which every AI feature is evaluated and instrumented for observability before release, cutting the path to a production-ready feature by an estimated 40%.
The challenge
A high-calibre engineering organisation wanted to accelerate how its teams build and ship AI features, but ad-hoc experimentation meant inconsistent evaluation, patchy observability and no shared bar for production-readiness. For a company whose own product is about measuring systems in production, shipping AI features that could not themselves be measured, evaluated and monitored was untenable.
What we built
Ran an AI development lifecycle programme tailored to an experienced engineering audience
Covered the full path from prototype to production: prompt and agent design, RAG, evaluation harnesses, observability and guardrails
Emphasised measurability and observability of AI systems in production, a natural fit for the org's domain, so every feature is monitorable once live
Delivered reference patterns and a lifecycle the teams could standardise on
Architecture
Outcomes
Cut the path to a production-ready AI feature by an estimated 40%, with every feature now evaluated and instrumented for production observability before release
Engineering teams aligned on a single repeatable AI development lifecycle
Evaluation and observability built into how AI features ship rather than added after incidents
A more confident, governed path from prototype to production
Results
44%
faster to production-ready
5→2 wks
feature lead time
100%
features instrumented
60+
AI features shipped
Other case studies
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