Personal platform for research, innovation, and AI education
Research-led AI work, public projects, and ideas designed to empower better decisions.
This site brings together Kayvan Zenouz's public work across AI strategy, technical experimentation, education, and open-source products. It is a personal platform alongside a full-time leadership role, not an employer microsite and not a generic consultancy front.
- Enterprise AI leadership
- Research and teaching background
- Selective public product building
What this platform is for
Research, innovation, education, and carefully chosen public builds.
ZENOUZ.ai complements a full-time role. It is where ideas become public work: experiments, writing, teaching, and products that aim to make AI more rigorous, more useful, and more empowering.
Positioning
ZENOUZ.ai exists as a personal platform for public work, ideas, and selected projects developed alongside a full-time role.
Featured project
ZenInvest is the flagship open project on the platform.
It brings together product design, model orchestration, human oversight, and a research-led approach to one of the hardest consumer AI domains: investing.
featured
ZenInvest
ZenInvest is an open-source AI investing system built to make research, screening, and execution more structured, transparent, and auditable for retail investors operating with human oversight.
Retail investing is constrained by information overload, research time, execution overhead, emotional decision-making, and opaque alternatives. ZenInvest is designed to reduce that burden without pretending that judgment can be outsourced entirely.
Capability Screens a large US equity universe with sector-aware discovery
Capability Combines market data, news, ratings, and model-generated reasoning
Capability Supports portfolio actions with logged audit trails and operator visibility
Why it stands out
- Open-source and inspectable rather than locked behind a proprietary black box
- Human-in-control portfolio decisions with explicit reasoning and risk constraints
- Designed as a research and product experiment with educational value, not a guaranteed-return machine
What this site holds
Three lanes of public work, each with a different role.
The platform is intentionally broad enough to hold research, public products, and educational material, but selective enough to stay coherent.
Enterprise AI leadership
Experience shaping AI strategy, operating models, governance, and delivery in complex regulated environments.
Research and education
A background in mathematics, academic research, and teaching that keeps the work intellectually rigorous and accessible.
Independent innovation
Public-facing experiments, open products, and applied thinking that turn ideas into systems others can inspect and learn from.
Project pipeline
A selective roadmap, not a content treadmill.
Only work that can be explained clearly and defended credibly gets published here. Everything else stays in development.
Studio Labs
A selective pipeline of future public projects spanning agentic systems, education, and human-centered AI tools.
- Curated roadmap rather than filler launch content
- Room for educational tools, case studies, and product experiments
- Focused on quality of problem selection rather than volume
About Kayvan
A personal platform shaped by leadership, research, and teaching.
Kayvan Zenouz is an AI and data science leader with a PhD in Mathematics, combining enterprise-grade AI strategy, academic depth, and hands-on product building in public.
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.
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.
- 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
Writing
Ideas in motion, with enough specificity to be worth reading.
The writing section is designed for essays, research notes, and practical reflections on AI systems, strategy, and education.
Building AI Systems That Leave Room for Human Judgment
A practical view of how to design AI systems that challenge, structure, and accelerate thinking without pretending certainty where none exists.
upcomingMulti-LLM Committees as a Product Pattern
Why distinct model roles can improve scrutiny, reduce blind spots, and create more trustworthy system behavior.
upcomingWhat Serious AI Strategy Looks Like in Practice
A grounded view of what it takes to move from AI enthusiasm to operational systems that teams can trust, govern, and actually use.
upcoming