Inato Innovation & Technology Culture

What’s your rule for fast, safe releases — and what KPI proves it works?

Our rule is simple: Make releasing so seamless that engineers focus on solving customer problems, not managing releases. We achieve this with a fully automated continuous integration/continuous development pipeline combined with feature flags for safety.

The process is straightforward: Start a branch from main; implement changes behind a feature flag; run automated tests; optionally request reviews; and merge to main and auto-release to staging and production. 

This setup lets us release to production more than 10 times a day while maintaining confidence and stability. Our key KPIs are deployment frequency and the low rate of production incidents, proving that speed and quality can go hand in hand.

What standard or metric defines “quality” in your stack?

For us, the gold standard is the change failure rate, or the percentage of releases that introduce production issues. It shows whether we’re maintaining quality while moving fast.

We also monitor mean time to recovery because failures are inevitable, but how quickly and safely we recover defines our resilience. Every incident sparks a blameless post-mortem where we capture lessons and update our processes.

While we don’t track every metric in real time, our goal is clear: Release quickly, detect issues early, recover fast, and improve continuously.

Name one automation that shipped recently and its impact on the business.

At the beginning of this year, we adopted Axiom, a real-time analytics and observability tool similar to Grafana, to bring deep visibility into both front-end and back-end performance. We use Axiom to aggregate key performance metrics and traces, from largest contentful paint for page speed to query-level data like N+1 queries and query waterfalls. 

With dashboards to visualize these metrics, Axiom helps us: Detect bottlenecks early and uncover hidden performance issues; prioritize work based on data, not assumptions; and measure the real impact of every change we deploy. For example, Axiom showed us that some of our key pages had poor performance, which led us to prioritize a dedicated optimization project. As a result, those pages are now loading more than twice as fast, directly improving the user experience. Thanks to Axiom, performance improvements at Inato have gone from being reactive and ad-hoc to data-driven, measurable, and continuous.