From Gut Feel to Evidence-Based Releases
4 engineering teams (~40 engineers)
8 weeks implementation
B2B SaaS Platform
Tech Stack:
The Problem
Engineering leadership had no objective way to assess release readiness. Decisions were based on "feels ready" intuition, leading to unpredictable production incidents and last-minute release delays. The team shipped 2-3 times per month but with high anxiety and frequent rollbacks.
The Approach
We designed and installed a release confidence operating model with measurable quality gates. This included: (1) defining 5 release confidence signals (test coverage, test stability, defect trends, environment confidence, change risk), (2) instrumenting CI/CD pipelines to calculate a release confidence score on every build, (3) establishing go/no-go thresholds based on historical incident data, and (4) training engineering leads to interpret signals and make data-driven decisions.
The Outcomes
- Moved from subjective "feels ready" to objective release confidence score within 2-4 weeks
- Reduced production incidents caused by untested changes by 40-65%
- Cut time spent in "should we ship?" debates by 30-50% (evidence replaced discussion)
- Increased release frequency from 2-3/month to weekly, with higher confidence
- Established repeatable quality gates that survived team turnover
What Changed
Before: Release decisions were made in last-minute meetings based on intuition. After: Every release candidate showed a confidence score (0–10) derived from test signals, defect trends, and environment stability. Teams shipped when score > 7.5 for standard releases, > 8.5 for high-risk changes. No more guesswork.
Services Provided
- •Release confidence framework design
- •CI/CD instrumentation and signal collection
- •Quality gate thresholds based on incident history
- •Team training on evidence-based release decisions
- •Ongoing refinement and signal tuning
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