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Expert interview with Kaleigh Moore
3 must-read articles for this week
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Partner | Lovable

Expert Interview | Kaleigh Moore

1. Tell me about your journey into content and why you're leaning into AI search?

My content journey started over 12 years ago, building a freelance career from scratch. Eventually, I grew it into an agency serving enterprise SaaS brands like Shopify Plus, IBM, Stripe, and Amazon Business. Along the way, I was contributing to Forbes and Adweek, co-hosting the Freelance Writing Coach Podcast for eight years, and doing the kind of long-form B2B SEO content work that compounds slowly but builds something real.

The AI search pivot happened because more clients started asking me for help solving the “How do we get cited by LLMs?” question. I saw that the way I’d been working for years starting to change (fast.)

The 'why' behind AI search specifically is harder to ignore every day. I'm also a grad student at Harvard focused on AI ethics and LLM information retrieval, so I'm studying how these systems actually surface information while I'm building them in practice. People aren't just Googling anymore; they're asking AI. And the brands that figure out how to show up in those answers are going to have a massive advantage.

2. What is the most important thing for content teams to understand about AEO?

The most important thing content teams need to understand about AEO is that it's not a technical problem, it's an editorial one.

AI models don't cite pages because they're well-optimized. They cite pages because they trust them. And trust, in this context, is built the same way it's always been built in journalism: through clear claims, specific evidence, credible sourcing, and authoritative structure.

3. You talk about "owning a topic." What does this mean for brands? How do they do it?

Owning a topic in an AI search context means becoming the source a model keeps returning to. Not just getting cited once, but being the default reference point every time a question in that space gets asked.

AI models don't evaluate pages in isolation. They evaluate sources holistically. So if someone asks ChatGPT a question about, say, email deliverability, and your brand has one solid post on the topic but nothing else — you might get cited. Once. But if you have deep, interconnected coverage across the full subject — strategy, segmentation, automation, analytics, benchmarks — you become the source the model keeps pulling from. That's the difference between a one-off mention and durable visibility.

So how do brands actually do it? A few things have to be true:

First, audit by topic, not by page. Most brands have years of keyword-first content that, when you actually map it, reveals they're deep in some areas and completely absent in others. You need to see the full picture before you can prioritize.

Second, identify your authority gaps — specifically where competitors are showing up in AI-generated answers, and you're not. An AI model that consistently pulls from your competitor on a subtopic you should own is a signal you can't afford to ignore.

Third, build in clusters and publish in clusters. Isolated posts don't build topical authority. Connected, comprehensive coverage does — with smart internal linking and an architecture that signals to AI models that your treatment of a subject is complete, not piecemeal.

And finally, measure it. Citation rate, mention rate, which models are surfacing you and for which queries — that data exists. The brands that own topics aren't just publishing more. They're watching the signal and iterating on it.