A closer look at how these results were delivered — situation, action, and outcome for three representative engagements.
The airline's QE function was fragmented across vendors, with recurring production incidents and no unified automation strategy across a large application portfolio.
Led an enterprise QE transformation: consolidated testing under a single operating model, introduced Chaos and Resilience Testing, and built a QE-Ops practice with continuous monitoring.
Zero production defects sustained over the engagement, 70%+ test automation, 99.99% system availability, and $1.2M+ in annual cost savings.
Manual, slow release cycles were limiting how quickly the client could ship updates to its e-commerce and retail platforms.
Architected an AI-infused test automation platform with autonomous test generation and self-healing scripts, integrated directly into the existing CI/CD pipeline.
Test cycle velocity improved by 30–40%, with a 25% gain in self-healing efficiency, reducing manual maintenance overhead.
Portfolio companies had inconsistent QE maturity and no shared view of where AI could meaningfully improve delivery economics.
Delivered AI QE advisory and readiness assessments across portfolio companies, identifying and prioritising AI-led opportunities by expected impact.
A roadmap targeting 20–25% SDLC efficiency gains and 40–60% throughput improvement across prioritised portfolio companies. Now a standing engagement type: AI and eval-maturity diligence for funds ahead of an investment or acquisition decision.
Happy to walk through how this would translate to your organisation.