Probabilistic vs. Deterministic
Those systems did exactly what they were designed to do. None of them likely failed a test. That's the problem.
Executive · AI Quality Strategist
Quality isn't a function.
It's a promise.
The consequences were always there. AI just made them impossible to absorb quietly. Twenty years of context driven quality work, applied to the problem that actually matters now.
Recognized
SIA Women in Security Power 100
Speaking
CAST Keynote · Agile Testing Days
Conference speaker · Workshop facilitator
Workshop
From Silos to Synergy
Building Cross Team Empathy to Strengthen Product Health
Scalable to external enterprise audiences
The Conversation
A series on AI quality — and what it actually takes to build systems that earn trust.
For engineering leaders who are asking the harder question. Published weekly on LinkedIn, extended on Substack.
Live
Calcing Zeros
Calcing zeros is really fast. The engine wasn't fast. It was doing almost nothing. And everything looked perfect.
Live
Familiar Models, Unfamiliar Problem
The question isn't how to adapt existing quality processes to AI. It's whether the foundation was ever adaptable enough.
Live
The Audit Problem
Nobody wants to own the definition of good. Without hearing from every stakeholder it affects, that ownership produces decisions, not clarity.
Upcoming
Production Drift
The system passed every test. Then the world changed around it.
Upcoming
Context Drift
Same system. Different context. Completely different behavior.
Upcoming
About
Context driven quality. Built for what AI actually is.
I've spent over twenty years in quality engineering holding a position that wasn't always popular. Quality is a thinking discipline, not a checking function. Context drives everything. Judgment where tools hit their limits. Human signal where automated checks go quiet.
AI didn't change that philosophy. It made the cost of ignoring it impossible to absorb quietly.
"Quality is quality. AI is the current domain."
The AI Quality & Trust Resilience framework is the result of applying that thinking to the full lifecycle of AI systems — from inception through production monitoring, across the four tracks that actually matter when the system you're testing doesn't behave predictably.
Current role
Director of Quality Engineering
DNSFilter · AI-powered DNS security
Framework
AI Quality & Trust Resilience
Full lifecycle · 7 phases · 4 AI specific tracks
Recognized
SIA Women in Security Power 100
Speaking & Advisory
For the rooms where this actually gets decided.
I speak to engineering leaders, C-suite executives, and product audiences about AI quality, production resilience, and what it takes to build systems that earn trust over time. Not theory — practitioner thinking from two decades of asking the harder question.
AI quality frameworks and what they actually need to cover
Production monitoring for probabilistic systems
Context drift and how organizations miss it
Building quality culture before the first model ships
What the deterministic quality model gets wrong about AI
Engagements
CAST
Conference of the Association for Software Testing
Keynote
Face It! Your Test Reports Suck
Agile Testing Days · 2022
Transforming test reporting into actionable business insights and customer centric quality strategies.
Co-taught · Intensive session
Transforming test reporting into actionable business insights and customer centric quality strategies.
From Silos to Synergy
Building Cross Team Empathy to Strengthen Product Health
Workshop · 2025
Workshop
Workshop · 2025
Available for 2026 keynotes and workshops. Inquire →
Contact
If quality is keeping you up at night, I'm interested in that conversation.
Speaking. Advisory. Or just a direct exchange about what's actually hard right now. No funnel. No pitch. That's not how I work.