ZENOUZ.ai
Email Kayvan
About

A personal platform needs a clear intellectual center.

ZENOUZ.ai is Kayvan Zenouz's personal platform for research, innovation, education, and selective public product building.

Profile

Kayvan Zenouz

AI and data science leader, researcher, educator, and hands-on builder

My work spans enterprise AI strategy, operating model design, production engineering, and agentic product development. I care most about systems that improve decision-making rather than automate thoughtlessly for their own sake.

Alongside a full-time leadership role, ZENOUZ.ai is where I develop public work: research-backed ideas, educational material, and carefully chosen products that reflect how I think AI should be built and used.

ZENOUZ.ai exists as a personal platform for public work, ideas, and selected projects developed alongside a full-time role.

Three pillars

The platform sits across leadership, research, and independent innovation.

These pillars are what keep the site coherent: strategic credibility, academic depth, and public experimentation.

Pillar

Enterprise AI leadership

Experience shaping AI strategy, operating models, governance, and delivery in complex regulated environments.

Pillar

Research and education

A background in mathematics, academic research, and teaching that keeps the work intellectually rigorous and accessible.

Pillar

Independent innovation

Public-facing experiments, open products, and applied thinking that turn ideas into systems others can inspect and learn from.

Selected proof

Selective evidence, not a resume dump.

The goal here is to show the shape of the work without turning the site into a corporate profile page.

Leadership Built and led a 10-person multidisciplinary AI team
Impact £2.05M realised commercial value across AI initiatives
Teaching Designed and delivered applied learning to 400+ students per term
Scope Strategy ownership across multiple business areas and AI use cases
Research and teaching

Research and teaching background

Kayvan's academic path spans a PhD in Mathematics, postdoctoral research, university teaching, and applied curriculum design across mathematics, machine learning, statistics, and data science.

Principles
  • Rigor before hype
  • Human judgment stays in the loop
  • Research should be useful, not performative
  • Systems should be legible and useful
  • Ambition should still ship cleanly