I am a researcher working at the intersection of AI, technology, and people. I started as an engineer with a background in machine learning, but moved toward research because I realized the hard part was rarely the technology. It was understanding the problem well enough to know what was worth building.
Today, I work on AI products, running studies, evaluating what we build, and helping teams make better product decisions.
I am a builder at heart. I shape my ideas through prototyping. I am also curious, which is probably why I have lived and worked in six countries in the last decade.
Research Interests: Evals for AI, Qualitative research, Quantitative research, AI product strategy, Foresight research, Customer research & benchmarking, Data strategy, Strategic planning, Human-Computer Interaction (HCI), Artificial Intelligence, Product Management, Strategy, Human factors, Agile methodologies, Mixed reality
Judging whether AI systems are actually good, and being clear about what the evidence cannot carry.
Evaluation of two RAG-backed AI chat surfaces at TOPdesk: a self-service assistant and an operator-facing Copilot. Four recorded sessions, 147 answers judged case by case with a stated reason on every verdict, clustered into a root-cause taxonomy and fifteen findings ordered by how much evidence sits behind each.
View Case StudyRigorous evaluation methodology for AI-powered nutrition advice, combining open coding of 100+ traces, custom error taxonomies, and systematic evaluation comparison.
View Case StudyStudies and frameworks that changed what got built.
Comprehensive user research mapping opportunities in incident management workflows through interviews, affinity mapping, and journey mapping with 10 participants.
View Case StudyReadiness and maturity framework benchmarked against Gartner, Ivanti, HDI, SDI, and Forrester — mapping where customers stand on the path to intelligent service delivery, informing product strategy, AI roadmap priorities, and long-term platform vision at TOPdesk.
View Case StudyThe tools and playbooks that let research reach decisions without a researcher in the middle of every one.
A copilot that lets any product role run real research on their own, so a decision does not have to wait for a specialist. Say or type what you are working on, and it routes you to the right task, grounded in TOPdesk's own product documentation. Twenty tasks across the research lifecycle, built as configuration rather than code.
View Case StudyAn internal tool, built and in use, putting six fragmented research and customer data sources behind a single plain-language query — qual and quant agents run in parallel, then a third synthesises findings shaped to the asker's role, so research data flows into product and AI strategy decisions.
View Case StudyAll eleven projects, including earlier doctoral and postdoctoral research →
If you are working on AI products, building out a research practice, or trying to get more decisions out of the research you already have, I am glad to talk.