Notes on building AI that ships.
Practical writing on building and shipping AI systems: the data underneath, the agents and RAG on top, the automation that runs it, the stores and sites it powers, and the search visibility that gets it found. What we learn building for clients, written down.
The range is deliberate, because we build the whole stack, not one slice of it. Some pieces cover AI search visibility (GEO), and they are structured the way we structure client content, with the answer first so assistants can quote them. The rest is simply what it takes to get data pipelines, agents, automation, and storefronts to production.
GEO vs SEO: what changes when the searcher is a model
SEO ranks a page for a person to click. GEO gets a passage quoted by a model. What actually differs, and what to do about it.
How to get cited by ChatGPT, Perplexity, and Google AI Overviews
A practical checklist for becoming the source an assistant quotes, platform by platform.
What llms.txt is, and whether it actually helps AI find you
A plain-text map for AI crawlers. What it does, what it does not, and an honest read on whether to bother.
Being findable now means being quotable by a model
Search traffic increasingly arrives pre-answered. What it takes to be the source an assistant cites, not the tenth blue link.
Building something with AI?
From data pipelines to agents to storefronts, we build AI systems and ship them to production. Tell us what's broken.