I help engineering teams move language models out of demos and into revenue. These are the essays, frameworks and post-mortems that came out of that work.
Every team I've watched fail at production AI failed in the same places — the retrieval seam, the eval seam, and the human escape hatch. The model was almost never the bottleneck.
On the slow decay of golden datasets, and what to do when prod drifts from your bench.
Treating your RAG layer as a UI decision, not a backend tuning problem.
Scope · Anchor · Audit · Loop — an updated diagram with notes on where teams stall.
Field-tested template for transferring an LLM system from prototype to platform team.
Brittle prompt, leaky context, drifting world — and the eval each one demands.
Eight years building ML systems — three of them spent prying language models into legacy enterprise stacks. I've shipped customer-facing LLM products at two Series-B startups and one very large bank.
This site is the public-facing notebook. Essays are long arguments, field notes are short ones, and the SAAL Framework is the diagram I draw at every kickoff. Everything here is freely re-usable — attribution appreciated, citation optional.