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Discovery has moved from links to conversations, and that shift is rewriting how customers find, evaluate, and buy. We sit down with Patrick Harrington, head of AI and machine learning at MetaRouter, to unpack generative engine optimization—what it is, why it matters, and how marketers can align content, consent, and speed to win in an LLM‑first world. Patrick draws on experience across Walmart, Block, and Workday to map a clear path from theory to practice: treat the first mile of data as your control plane, capture consent at creation, and design information that LLMs can confidently retrieve mid‑conversation.
We break down the real differences between SEO and GEO, moving past keywords into the geometric language models use to represent meaning. That means structuring product data, claims, comparisons, and imagery so they form unique, verifiable signals. It also means tightening provenance, third‑party validation, authoritative domains, and fresh timestamps, so models favor your content and users trust the answers they see. Along the way, we tackle hallucinations as a quality problem, the role of guardrails, and why bringing models to your data inside your cloud keeps sovereignty intact while unlocking real‑time decisions that boost ROAS.
Finally, we zoom out to the market dynamics ahead. Expect a push and pull among LLM platforms, retailers and brands, and the advertising ecosystem. The advantage goes to teams that can pass actionable context across channels without surrendering control: durable first‑party IDs, consent‑aware event streams, and omni-channel signals that reflect how people really shop. If intent lives in conversation, optimization must live in the first mile, where you decide to route or suppress, personalize or protect, all in milliseconds.
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