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Quality & DevOps Advisory
Know where your quality practice stands, and what to fix first
A short, evidence-based audit of how your teams test, release and learn from failures, with a transformation roadmap from QA to QE that runs inside the DevOps pipeline.
Client result
3.4x12-month ROI from an AI-led quality engineering and PMO strategy
Global retailer
Read the case studyfrom idea to impact, across the systems lifecycle, this service sits here
Maturity
The journey from testing to quality engineering
Read the five levels, then take the quick check to see where your main product likely sits today.
- Level 5You are likely here
Quality intelligence
AI generates and repairs tests and flags risky changes, releases are routine, and speed, stability and security are reported in business terms.
- Level 4You are likely here
Continuous quality
Every change is checked for function, performance and security, infrastructure is code and small releases ship behind feature flags with fast rollback.
- Level 3You are likely here
Quality engineering
Testers work inside the sprint, and CI/CD pipelines deploy to test environments with code and dependency scans gating every release.
- Level 2You are likely here
Automated testing
Regression suites and a CI build run on every change, but tests break often, deployments are scripted by hand and security scans are occasional.
- Level 1You are likely here
Manual QA
Testing and deployments are manual and happen at the end, security is a review before go-live and release dates move when problems surface.
Quick self-check
Five questions give a starting point. The audit scores each product on evidence from your own repositories, pipelines and defect history.
Request the Quality Maturity AuditWhat we do
Maturity areas of focus
across the enterprise landscape.
AI-accelerated
AI-led quality assessments
Analyze the current state, including test assets, pipelines and defect history leveraging AI.
Test asset analysis
Coverage, duplication, flaky tests and dead tests across your suites.
AI-acceleratedDefect pattern mining
Where escaped defects come from, by component, change type and team.
AI-acceleratedPipeline review
Where releases wait, which gates exist and which ones get skipped.
Team interviews
How developers, testers and product owners actually work, beyond the process document.
QA to QE roadmaps
A staged plan to move from a separate QA function to quality that is engineered into delivery.
Target operating model
Who owns quality at each stage, and how testers, developers and SREs share it.
Tooling and frameworks
Automation frameworks, test data and environments that fit your stack.
Skills plan
Training and hiring to move manual testers into automation and engineering roles.
Measures that matter
Quality reported as escaped defects, recovery time and customer impact, not test counts.
Lean QA and DevOps
Reduce the tests that add little confidence, and the delays that slow every release.
Regression right-sizing
Retire or merge tests that no longer catch anything, so suites finish sooner.
AI-acceleratedRisk-based release gates
Gates that scale with the size and risk of each change.
Environments and data on demand
Test environments and masked data that teams provision for themselves.
Improvement loop
A monthly review of incidents and escapes that feeds the backlog.
The Quality Maturity Audit
Let’s get your audit started
Most of the analysis runs on data you already have.
Collect and listen
We agree on the two or three products in scope, take read access to repositories, pipelines and the defect tracker, and interview six to ten people across roles.
Analyze
AI-assisted analysis of test assets, defect history and pipeline timings, with maturity scored per product and backed by evidence.
Recommend
A roadmap across three horizons, quick wins your team can start straight away, and a readout to engineering and business leaders.
You get
A maturity level per product, the evidence behind it and a sequenced roadmap with owners and expected impact.
Request the audit
Case study
Retail & Consumer Goods | Global retailer
AI-led QE and PMO strategy
The business had just gone live on SAP and Monday.com, alongside ServiceNow, web and mobile. Testing happened in production and QA was manual and reactive. There was no PMO, so initiatives were funded without a shared view of ROI.
We ran a 12-week, AI-led QE assessment covering test coverage, risk and automation, maturity benchmarking and tool selection, which led to a target-state QE blueprint, rollout roadmap and ROI case. Alongside it, we set up a PMO with a weighted value-scoring model, prioritization matrix and portfolio value dashboard that charts every initiative against customer outcomes.
Find out where your releases lose time
Three weeks, a handful of interviews and a roadmap built on your own data.