Work
Case Studies & Writing
Projects that shipped to production and thoughts on building AI systems.
Case Studies
Multi-Agent FDA Document Review
6-agent AI system for FDA document review - 60-70% time savings, multi-million USD ROI
AI Keyword Classifier
Rules-first AI classification - 50 hours/month saved, <$1 per run
AI Translation Pipeline for Pharma
AI translation integrated with Veeva Vault - 2+ week turnaround reduced to hours
Enterprise Data Governance Transformation
€400K engagement, 25+ stakeholders, 7 data domains mapped across 13 countries
When to Halt a Migration
400,000+ documents profiled, 92% scope reduction, migration halted - sometimes stopping is the right answer
Product-Led Growth Engine
0 → 1,000+ users, 100% retention, 8-system commercial stack built from scratch
GSC → BigQuery Pipeline
60 customers, 53M+ rows, solving GSC's data retention limitation
Articles
Your AI Strategy Is Collecting Dust
Most AI strategies fail before implementation starts. Not because the ideas are wrong - because the strategy was built for a board deck, not for the people doing the work. Here's how to build one that actually gets executed.
AI Brain Fry Is Real - But It's Not the Tools' Fault
Harvard Business Review says AI is frying workers' brains. My data - 1,986 commits and 1,900 AI sessions in 76 days - shows the opposite is possible. The difference isn't the tools. It's whether anyone invested in actually understanding them.
Your agents know exactly what you tell them
Most people's AI instructions are empty or stale. The ones getting consistently better results are transferring judgment, not just preferences - and they have a system for keeping it current.
Meta Prompting: Let AI Write Your Prompts
The most underused technique in AI: using the model to improve your inputs. How to generate, critique, and refine prompts using the AI itself.
What Are AI Agents, Actually?
An agent is a system where the model decides the next step. That's it. Most things called 'agents' aren't - they're workflows with LLM-powered steps, which is usually the right architecture anyway. Understanding the actual distinction helps you buy smarter and build better.
What Are LLMs, Actually?
Large Language Models explained without hype or jargon. The 4,096-dimensional mental model that explains hallucination, context windows, and how to use them well.
How I Learned to Build
From port forwarding at eight years old to building AI systems for pharma - a twenty-year journey of refusing to accept limitations.
What Are Workflows and Agents
The distinction that matters is simpler than the terminology suggests. In a workflow, you define what happens. In an agent, the model defines what happens. Understanding when to use each - and how to combine them - is most of what separates successful AI implementations from expensive experiments.