AI that does something real.
Most "AI features" are a chatbot bolted onto a form. I build systems where AI does real work — inside real products, with the observability and governance to trust it.
Most AI integrations fail because they're bolted on, not built in.
Failure: No feedback loop
A model output goes straight to production with no way to catch drift or bad calls.
Failure: All-or-nothing automation
Teams either automate everything and lose control, or automate nothing and get no value.
Failure: No provenance
Nobody can explain why the AI did what it did, so nobody trusts it.
Approach: Evaluation built in
LLM-as-judge and human review loops catch problems before they reach users.
Approach: Human-in-the-loop routing
Policy-based routing sends the mechanical fixes through automatically and the judgment calls to a human.
Approach: Full observability
Every decision traces to its source — inputs, reasoning, and output are logged and reviewable.
What I Build
LLM Integrations
OpenAI, Anthropic Claude, Google Gemini — wired into your product, not a sandbox.
Agentic Pipelines
Multi-step, multi-agent workflows that handle real tasks end to end.
RAG Systems
Retrieval-augmented generation grounded in your actual data, not hallucinated.
Human-in-the-Loop Workflows
Governance and approval routing so AI output never ships unchecked.
AI-Powered SaaS Features
AI as a first-class feature in your product, not a side experiment.
Process Automation
Replace manual, repetitive processes with AI-driven automation.
Tech Stack
OpenAI API · Anthropic Claude · Google Gemini · Mastra · LangChain · Node.js · Python · n8n · Make.com · Supabase/Postgres · AWS
Featured Case Study
ContentGuardian
An auto-healing content platform that detects when source knowledge changes — and fixes itself, with human-in-the-loop governance.
Case Study →