Entities, context, and the AI semantic field: how Google and AI engines understand your business

Being visible is no longer enough: your company needs to be understood. For an SMB, the battle now hinges on clearly identifying entities (who you are, what you sell, to whom), providing context (customer problems, use cases, locations), and maintaining semantic coherence across the site. This trio—not keyword lists—feeds your AI semantic field and makes your brand readable by Google and generative assistants.

LSI: a legacy acronym, not to be confused with modern semantics

“LSI” (Latent Semantic Indexing) refers to a 1990s academic technique for relating texts via term co‑occurrence. In SEO discourse, people long advised using “LSI keywords.” Problem: Google does not document LSI as a ranking lever, and its official ranking systems make no mention of it (they cite PageRank, freshness systems, BERT/MUM for understanding, etc.). Spokespeople have also indicated that “LSI keywords” is not a relevant notion; several reputable publications have reiterated this by citing these public statements. In other words: stop chasing “LSI keyword lists.” Focus on entities, relationships, and editorial clarity.

Entities and context: the building blocks of understanding

An entity is a uniquely identified object: a company, a person, a service, a place. Google represents these objects in its Knowledge Graph, which assigns identifiers and types to entities. Its public documentation (Knowledge Graph Search API) sheds light on this logic of objects and links. On your site, structured data (JSON-LD markup mostly based on schema.org) helps Google interpret who you are, what a page contains, and which presentation you may be eligible for in results. Google is clear: markup does not guarantee better rankings, but it does make you eligible for rich results and clarifies content meaning.

This understanding doesn’t rely solely on markup. Google’s systems analyze text, images, internal links, and leverage language models (BERT, MUM) to better interpret queries and content. On quality, public guidance confirms that usefulness and trustworthiness come first, regardless of whether content is written by hand or assisted by AI.

AI semantic field: an operational definition for SMBs

By “AI semantic field,” we mean the organized ecosystem of concepts, entities, attributes, questions, and evidence that describe your business from a search engine’s point of view. Concretely:

• At the center: your “company” entity (legal name, brand, SIREN, addresses, executives, sectors).
• Around it: your offers (types of services/products), your use cases, the customer problems you solve, your geographic areas, your proof points (references, reviews, cases).
• At the edges: upstream/downstream search intents (comparison, pricing, timelines, support), industry synonyms, and constraints (B2B/B2C, standards, languages).

A heating contractor, for example, becomes more legible by explicitly linking “gas boiler maintenance,” “24‑hour repair,” “annual contract,” “service areas,” “supported brands.” The goal isn’t to stack terms, but to link relevant concepts within a clear architecture and useful content.

Information architecture and editorial consistency

Semantics is embodied first in your site structure. A few simple principles make a big difference:

• One page = one primary intent: avoid catch‑all pages. Favor clear templates like “service,” “industry,” “case study,” “resource.”
• Explicit relationships: contextual internal linking; flows like “problem → solution → proof → contact.”
• Structured data: Organization, LocalBusiness, Product/Service, FAQPage where relevant.
• Consistent terminology: avoid naming the same service differently across pages; it’s a common source of ambiguity.

To (re)think this architecture, start with Marketing First: positioning, messages, proof, then tools. A website project should always begin with strategy and the target experience—not with templates. If you’re preparing a redesign, start with a business framing: see our marketing strategy approach and our websites methodology.

GEO visibility and generative engines: what changes with AI Overviews

Since May 2024, Google has been rolling out AI Overviews (formerly SGE) in the United States—generated summaries that aggregate sources and help users explore a topic. Google notes these features build on its existing ranking and quality systems, so SEO best practices still apply. On measurement, documentation has clarified how these displays are counted in Search Console within the “Performance” report (methodological clarification).

In this context, your “AI semantic field” is doubly useful: it improves how your pages are understood and increases the likelihood of being cited by generative engines. Again, prioritize value: practical guides, honest comparisons, problem‑oriented FAQs, verifiable proof. To go further on AI applied to content and search, see our page on AI, RAG & vector search.

A pragmatic action plan for an SMB

1. Name the key entities: company, brands, services, executives, addresses, areas, partners. Gather proof (references, certifications, reviews).
2. Map the AI semantic field: customer problems, intents, synonyms, common questions, existing content. Keep it simple and documented.
3. Structure the site: intent‑based IA; clear page templates; explicit internal linking “problem → solution → proof → CTA.”
4. Implement relevant structured data (Organization, LocalBusiness, Product/Service, FAQPage) in JSON-LD.
5. Write for the user, not for a tool: Google’s documentation rewards quality and experience (E‑E‑A‑T), regardless of production method.
6. Measure: Search Console (queries, pages, enhancements), tracking clicks from generative elements, CRM consolidation to connect traffic and opportunities. On acquisition, stay strategy‑led: see our SEO & GEO page.

AI: copilot for your judgment, not autopilot

AI can speed up semantic research, help inventory customer questions, and generate headline variants. But automation without intent mostly produces noise. Define the scope, fact‑check, sign your content, and tie every deliverable to a business objective. That’s Frametonic’s philosophy: marketing vision + creativity + digital + AI in service of a strategy—not the other way around. For a guided implementation, contact us here.

Short questions, direct answers

Does LSI still help with SEO?
No. The term is outdated and does not appear in Google’s ranking systems documentation. Focus instead on entities, relationships, and editorial quality.

Do I have to mark up all my pages with schema.org?
No, but appropriate markup clarifies your entities and can enable rich results; it’s recommended on key pages (company, services, products, FAQs).

How do I “feed” my AI semantic field?
With useful, intent‑aligned content: guides, comparisons, case studies, FAQs, and internal links that connect problems, solutions, and proof. Stabilize terminology and avoid duplicates.

Do AI Overviews change my strategy?
SEO best practices still apply. Produce reliable, structured, measurable content. Google has clarified how these displays are counted in Search Console; monitor your reports.

AI‑generated content: forbidden or accepted?
Google says it rewards quality and usefulness first, regardless of production method. Avoid value‑free automation and follow anti‑spam policies.

To anchor your actions in business outcomes and visibility, also see: Marketing strategy • SEO & GEO • AI, RAG & vector search.