Work
The AI systems I've designed and shipped to production: MCP infrastructure, agentic anomaly detection, RAG, observability. The problem, what I built, and what it changes.
Case studies
Every project starts from a concrete need. Here's what I built, and what it changed.
AI infrastructure · Security
Give AI agents access to internal tools without opening a hole.
An MCP infrastructure that decouples LLMs from executing information-system tools: a Zero Trust / SSO proxy in front of every tool, secrets never exposed, and observability on every LLM call.
Every LLM wired to each internal tool by hand
One protocol exposes the tools to agents, under access control
Anomaly detection · Agents
Detect capacity anomalies, and have agents explain them.
A three-engine detection system (statistical, machine learning, hybrid) across multi-OS fleets, with LLM agents orchestrated by LangGraph that explain and prioritize the anomalies. Interactive dashboards and automated PDF reports.
Capacity thresholds watched by hand, alerts with no context
Three-engine detection, explained and prioritized by agents
RAG · Knowledge base
A technical knowledge base that answers, always in sync with the code.
An end-to-end RAG chain feeding the internal AI assistant, and an idempotent synchronization service between the technical GitHub repositories and the knowledge base.
Docs drifting from the code, answers hunted down by hand
An assistant answering on a continuously synced base
Observability · LLM
Catch the anomaly the moment the log is ingested.
A native Logstash filter, in Ruby, that calls an LLM during log ingestion and returns anomalies as structured JSON, ready to use.
Anomalies lost in the volume, caught after the fact
Detection at ingestion, structured and ready to use
Work done on assignment. Full context on a call.
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