I build eval-driven AI product systems β from PRDs and workflows to demos, launch gates, and LLM evaluation loops.
Most of my work sits across AI Product Management, enterprise data platforms, LLM evals, agentic workflows, and product operating systems.
π Portfolio: aboutrichie.vercel.app
- π§ Product-Management_OS AI PM operating system for PRDs, evals, product reviews, stakeholder updates, and daily execution.
- π° Finance-bot AI finance assistant prototype for structured analysis, reasoning, charting, human review, and eval workflows.
- π§βπ» aboutrichie - Live site Interactive AI PM portfolio and personal product profile.
- π llm-comparator Side-by-side LLM response comparison and evaluation workflow exploration.
- π§ͺ LLM evals, rubrics, failure modes, and quality gates
- π’ Enterprise AI workflows, governance, and trust
- π Data platforms, Customer 360, CDP, CRM, and semantic layers
- π€ Agentic workflows for product teams
- π PRDs, launch reviews, product strategy, and stakeholder updates
- π οΈ AI builder workflows using Claude Code, Codex, Gemini, GitHub, Python, and SQL
Problem β Workflow β Data β Model β Eval β Launch β Learning
AI products should not stop at demos. They need clear user problems, trusted workflows, quality gates, human review, and measurable business impact.
Iβm building a public AI PM portfolio around:
- π§ Product Management OS
- π§ͺ LLM evaluation systems
- π’ Enterprise AI workflows
- π Data platform to AI product loops
- β Launch readiness and quality gates
- π Portfolio: aboutrichie.vercel.app
- πΌ LinkedIn: linkedin.com/in/richieriri
- π¦ X: @richieririeth
- π§βπ» GitHub: github.com/richardan01


