I design, debug, and ship production-grade LLM systems where reliability, evaluation, and failure handling actually matter.
Most AI projects fail after the first demo. I focus on the part that comes after.
I work at the intersection of agentic workflows, retrieval systems, and backend engineering.
My job is turning ambiguous problems into systems that run without babysitting.
A few end-to-end systems shipped to production—built for reliability, not demos.
Reduced time-to-shortlist by letting agents disagree before deciding.
In production. Used daily. Evaluated continuously.
Visit Teamcast.aiReduced review time from minutes to seconds with agent-assisted triage.
In production. Used daily. Evaluated continuously.
View project detailsLet agents research, qualify, and route leads without manual follow-up.
In production. Used daily. Evaluated continuously.
View project detailsIntelligent systems that solve real business problems.
Multi-agent systems, RAG, and intelligent automation.
End-to-end AI products from concept to deployment.
A sophisticated AI system that automates lead discovery and qualification across 12+ data sources, delivering 3x more qualified prospects with 80% reduction in manual research time.
Notes from building systems that didn’t work the first time.
AI just crossed the line mathematicians thought was safe. What Google DeepMind's Aletheia research agent means for frontier mathematics — solving open PhD-level problems and why Terence Tao called AI his junior co-author.
Read on MediumJensen Huang rode shotgun. The car drove itself. Why Nvidia's dual AV stack demo matters — and what happens when you run neural networks and rules-based systems simultaneously.
Read on MediumContext control, backup, topic routing, and the safety lesson everyone missed. What actually matters from the OpenClaw update.
Read on Medium