Is SEO Becoming About Teaching AI Who You Are?

A 2023 Google patent describes how AI systems could build an understanding of businesses, brands, products, and other entities from websites and public data. The filing outlines a process for extracting information, identifying relationships, and synthesizing what Google calls a deep, holistic characterization of an entity.

What Happened

Search Engine Land's Rich Sanger reported on June 22, 2026 on a Google patent titled Data extraction using LLMs. The patent describes a system that identifies a domain and associated entity, gathers content from webpages and public sources, then uses a large language model to interpret that information and form an understanding of the entity, including its attributes and relationships, rather than simply indexing documents.

Why This Matters for B2B Marketing Leaders

If Google's search products increasingly recommend, compare, and explain brands rather than return blue links, your visibility depends on whether AI systems can accurately characterize who you are. For HR Tech and FinTech marketers, this is a category problem. When a buyer asks an AI assistant to suggest a payroll platform for a 500-person services firm or a compliance tool for a regional bank, the model pulls from an entity profile built across your site, review platforms, analyst coverage, press, and partner mentions. If your owned content does not clearly establish category, clients served, differentiators, and proof, the AI fills the gap with whatever public signal is loudest, which is often a competitor.

The Starr Conspiracy's Take

You cannot optimize a document your way into an AI recommendation if the model does not understand what your company actually is. The patent confirms what we have been telling clients: entity clarity is now a precondition for demand capture. That means consistent category language, structured data, named clients, named integrations, and a knowledge graph that connects your brand to the problems you solve. We walk through this shift in our breakdown of answer engine optimization for B2B brands, which reframes SEO work around what AI needs to confidently say your name.

What to Watch Next

Watch for Google to surface entity-level confidence signals inside AI Overviews and for review platforms like G2 and Gartner Peer Insights to gain weight as entity training inputs. The likely next 12 months bring agency pitches built entirely around entity optimization. Decision point: who on your team owns brand entity hygiene?

Related Questions

How is entity-based SEO different from traditional SEO?

Traditional SEO optimizes individual pages to rank for keywords. Entity-based SEO works to ensure AI systems form an accurate, complete understanding of your brand across all public sources. You are shaping a characterization, not a ranking.

What should HR Tech marketers do first to prepare?

Audit how AI assistants currently describe your company, your category, and your closest competitors. Gaps and inaccuracies in those answers tell you exactly what entity signals are missing. Our guide to building AI search visibility outlines the audit framework.

Do reviews and third-party content matter more now?

Yes. The patent describes synthesis across websites and public information, which includes review platforms, analyst reports, and partner sites. Your owned content sets the frame, but third-party signals confirm or contradict it, and AI weights confirmation heavily.