Our CoE packages years of delivery experience into repeatable practices. We tailor templates and governance to your applications, then scale from manual regression discipline through automation and performance engineering.
We implement process and standard practices first so suites deliver end-to-end coverage. Evaluations and sign-offs follow clear, auditable workflows your stakeholders can trust.
After a baseline manual suite exists, we run a feasibility study, select sustainable tooling, and build automation with maintainability and release cadence in mind.
We establish baselines, identify bottlenecks, and align technical results with business SLAs — including endurance and spike scenarios where reliability under load matters.
We help identify vulnerabilities affecting customer, business, and supplier data so gaps can be remediated before exposure in production.
Requirement analysis and knowledge transfer so each application and its business use is understood before planning begins.
Test approach, suite structure, and test management tooling aligned to your release model.
Straight, core-regression, and full-regression sets prepared with consistent templates and traceability expectations.
Runs aligned to releases with evidence that behaviour matches agreed requirements.
Defects prioritised with development, fixed, re-tested, and signed off before promotion to UAT or production.
Suites updated as applications evolve — functionality and test data kept current.
Understand application suites, release cycles, and QA needs; define SOPs so build approvals follow standard processes.
Adapt proven templates from requirement understanding through planning, design, and execution for your engagement.
Build straight, core-regression, and full-regression suites; start requirements traceability mapping for business coverage.
Execute baseline runs; address compatibility and accessibility gaps where required; finalize traceability.
Organize suites and artifacts in your test management tool (for example Jira-based workflows) across releases.
Assess automation candidates, tooling fit, and maintenance expectations before investment.
Automate agreed cases to the current production baseline per application, aligned to the feasibility study.
We validate experiences across desktop and mobile targets your users actually rely on.
We balance cloud device access with in-house labs where needed — weighing setup cost, maintenance, security, and speed when production issues require new coverage.
Performance evaluations confirm stability under realistic load and clarify where engineering must act.
Identify UI flows, APIs, and integration points to evaluate; observe production-like behaviour where available.
Agree business SLAs and success criteria for the evaluation window.
Build scripts, run baselines, and document bottlenecks with reproducible evidence.
Development remediates issues; we re-run to confirm SLAs are met.
Sustained load over long windows (often five to eight hours) to surface memory, connection, or degradation issues.
Sudden load increases to see how critical metrics behave when traffic spikes unexpectedly.