Writing
The Data Engineer Should Have Come First
When enterprise leaders build AI teams, they almost always hire data scientists first. That sequencing mistake — hiring for insight before hiring for infrastructure — is why so many AI initiatives stall between POC and production. Building an AI-ready organization requires a deliberate hiring order, and the data engineer comes first.
You Don't Have an AI Problem. You Have a Context Problem.
Enterprise AI proof-of-concepts routinely impress in demos and collapse in production — not because the model was wrong, but because the POC was fed curated context that doesn't exist at scale. In any large enterprise with years of production technology behind it, the knowledge that matters most lives in code, configuration, and people's heads — not in documents any AI can read. Two real deployment stories and a three-phase framework for closing the context gap before your next pilot hits the production readiness wall.
How to Use LLMs in Deterministic Enterprise Systems: Hard-Won Lessons from the Trenches
Use LLMs as a compiler front-end; keep your deterministic pipeline as the back-end. Constrain outputs to structured schemas, freeze dependencies, build agentic validation loops, and keep humans at decision points. These are the patterns that emerged from real attempts, not theoretical frameworks.