What I Build
Building retrieval systems that surface the right context at the right time.
Multi-agent workflows with LangGraph, Autogen, and custom frameworks.
Connecting LLMs to APIs, databases, and external systems.
Building metrics and benchmarks to measure AI system performance.
Customizing models for domain-specific tasks and behaviors.
Shipping AI systems that scale reliably in production environments.
Systems & Solutions
CRE valuation and analyst workflows needed the same AI capabilities from a catalog UI, Slack, and Claude Desktop — not a copy of the logic in each surface.
Vacation-package booking was split across chat, voice, and a web UI that did not share state.
Surgeons needed hands-free dictation in the OR that landed as a structured operative note they could edit by voice.
Financial analysis lived in separate databases, vendor APIs, and charting tools, so answering a question meant stitching those sources together by hand.
QA reviewers compared lot-grouped manufacturing PDFs by hand — compounding, filling, assembly, packaging — without checks tied to page-level evidence.
Underwriters had to click through legacy government portals by hand to verify policyholder data, so quote work stalled on verification.
The client needed to audit thousands of unstructured ESG reports to identify high-risk investments, a process previously requiring weeks of manual analyst labor.
SRE teams were overwhelmed by alert fatigue from thousands of non-critical logs, missing actual root causes during production outages.
Engineering managers were reconstructing blockers by hand across fragmented Slack and Jira threads.
Where I Show Up
I present at DEVx's AI paper club — a presenter-led deep dive into papers that matter for building systems.
Recent talkI host a monthly AI Happy Hour at North Park Beer Co. Each month I pick an optional discussion theme — recent ones include inference chips, LLMWiki, and rising token costs.
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