I help revenue operations teams fix the processes, systems, and data that slow them down. I do the consulting work — process design, systems architecture, reporting that leadership actually uses — and I also do the build work: AI tooling and automation platforms let me ship the integrations, dashboards, and internal tools that turn a recommendation into something running in production in days, not months.
- Operations Consulting (RevOps) — process design, systems architecture, revenue data flows, and reporting that leadership actually uses
- AI-assisted development — I direct fleets of coding agents to design, build, review, and ship real software, which is how a solo consultant delivers production systems instead of slide decks
- Automation — Zapier, n8n, and custom agentic pipelines that take manual ops work off your team's plate
- Data & Analytics — SQL, Python, BI: the foundation everything else is built on
Contract-to-cash automation for a B2B SaaS vendor — found a hidden 19-day delay in payment collection that no system was reporting, proved it in cash terms, then rebuilt the client's quote-to-cash lifecycle so the gap can be chased on day one. Full architecture, decisions, and anonymized code inside.
I run my own infrastructure and build products on it — nearly all of it designed, shipped, and maintained by AI agents under my direction:
- Live web products — full-stack apps in learning and travel, in production with real users
- Agent-orchestrated homelab — a self-hosted cluster where agents deploy, monitor, and maintain the services they run on
- Autonomous pipelines — agent systems that research, decide, and execute end-to-end on schedules, no human in the loop
- A living knowledge base — a second-brain vault continuously curated by scheduled agents
Before consulting, I spent years as a senior data analyst — advanced SQL, Python (pandas, scikit-learn), Power BI / Tableau / Looker, ETL pipelines. That backbone shapes how I approach operations problems today.
- 🔍 SQL Query Optimization Suite — this is what my SQL looks like under the hood: real business problems, production-grade query design, execution times cut 60%
- RevOps engagements where AI leverage shortens the road from recommendation to running system
- Automation systems across Zapier, n8n, and custom orchestration
- Open-sourcing selected pieces of the agent infrastructure I run every day