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Entity & knowledge modeling agency Based in Europe We resolve fragmented company information into the entities, relationships & corroborating evidence that define how Google & AI search understand a company.
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We turn a company’s entity model into website architecture, structured data, external evidence & ongoing representation monitoring.
Optimizing a company’s Entities and Knowledge Graph creates the context Google and AI search need to understand when the company is relevant to a query. Here’s how we do it.
We build a Company Information Model before we optimise how the company is represented.
Your website is one of many places that describe the business. We bring product, audience, category, customer, partner and source information into one working view, with each fact marked as confirmed, inferred or still open. This gives the website, schema and external work the same source of truth.
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We resolve entities, then connect them through the relationships that explain what the company does and where it belongs.
Product names, feature language and category definitions rarely match across every source. We reconcile the important facts, record unresolved ambiguities, then map how products, features, capabilities, problems, use cases and audiences relate. This makes the company’s market position and the proof behind it clearer.
We use the model to decide what the website needs to say, where it needs to say it, and how related information should connect.
The work becomes practical website decisions: what to create, improve, merge or leave without its own URL. We make important associations clear through content, navigation and internal linking. Structured data then reflects one connected model of the organisation, its people, products and pages.
We strengthen the independent evidence behind the claims and relationships that matter to your market position.
Self-published statements do not carry the same weight as independent support. We identify the relationships that need stronger proof and the sources best placed to provide it. Customer, partner, publication, analyst and database coverage then become focused evidence priorities.
Search engines, LLMs and agents retrieve and weigh information differently, but they all depend on a company being clear, consistently represented and supported by evidence.
We partner only with B2B software companies, bringing accumulated knowledge to the complex entity structures that define their products, markets and websites.
SaaS
Fintech
AI
Enterprise Software
AI search makes entity relationships part of the retrieval layer & therefore a critical part of the AI search chain.
LLMs generally do not evaluate every company from zero for every prompt. They retrieve from an existing information environment: what a company is associated with across categories, capabilities, use cases, buyers, industries, integrations and customers, together with the evidence connecting those relationships. Retrieval also varies by model, prompt, context and time, so the same company can be represented differently from one answer to the next.
The more clearly and consistently those associations are established and corroborated, the easier it is for an LLM to determine why a company belongs in the candidate pool behind an answer. Being retrieved is not the same as being recommended: a company’s page can be used as a source while the company itself is never named.
When those relationships are missing, fragmented, ambiguous, or supported only by the company’s own claims, the consequence is not simply a lower ranking.
The company can be excluded from consideration altogether, even when it is highly relevant to the query, because the connections required to retrieve it were never sufficiently established.
It may not reach the candidate pool if its information is inaccessible, may not survive reranking without contextual relevance, and may not reach the final answer without corroboration or strong enough entity signals.
That is why entities matter more as search becomes answer-first. Companies increasingly need to build the associations they want to own, then reinforce them across their website, customers, partners, publications, databases, and other independent sources.
Questions & Answers.
Entity SEO is the work of making the things that define a company, including its products, capabilities, customers, categories, use cases and people, clear, consistent and connected across search engines and AI systems.
It focuses on the relationships between those things, not only the keywords used on individual pages. It also covers basic company facts such as founders, locations and the legal entity behind the brand.
An entity is a distinct thing that search systems can identify and connect to other things. A company, product, feature, customer, category, integration or use case can all be entities.
Each has attributes and relationships that help explain what it is and why it matters. Systems resolve an entity by matching names, identifiers and attributes across sources, so consistent representation makes that resolution easier.
Knowledge graph optimisation is the work of making a company’s important entities and relationships easier for search systems to understand and represent accurately.
It includes defining the company model, connecting its products and market context, representing those relationships on the website and strengthening the evidence behind them.
Google’s Knowledge Graph is the best-known example, while AI systems build their own representations from the information they retrieve.
Semantic SEO improves the meaning, context and topical depth of content. Entity SEO works at the company level: it clarifies what the company, its products, customers, categories and capabilities are, and how they connect across the website and wider web.
Semantic SEO strengthens what a page means; Entity SEO strengthens what the company is.No. Synthetic prompt tracking shows where a company appears, but not necessarily why, what the commercial value is, or what should happen next.
Useful decisions require broader evidence from competitors, citations, technical accessibility, traditional search, website behavior, attribution, conversions, and revenue.
No. Schema markup can express selected entities and relationships in a machine-readable format, but it cannot decide what the company is, resolve conflicting descriptions, determine which relationships matter or create independent evidence for a claim.
Schema follows the company model; it does not replace it. It is most useful when it describes one connected organisation, with stable identifiers, links to official profiles and the same facts shown on the page.
Omnius works with B2B software companies, including SaaS, fintech, AI and enterprise software businesses.
The service is designed for companies whose product architecture, use cases, integrations, buyer context and customer evidence need to be made clearer across their website, search presence and external sources, especially when names, categories or company facts differ between sources.
An engagement can include a Company Information Model, entity and relationship mapping, website and internal-link architecture, structured-data architecture and implementation guidance, an External Corroboration Map, coverage analysis and a prioritised roadmap.
It can also include reconciling external company records and monitoring how search and AI systems describe the company. The exact scope follows the company’s market, existing representation and commercial priorities.
Entity SEO gives SEO, GEO and AI search work a clearer foundation. Once the company’s products, relationships, pages and evidence are defined, Omnius can use that model to guide content, technical SEO, structured data, digital PR, visibility monitoring and AI search representation.
Omnius is a B2B SEO & GEO agency; partnering up exclusively with SaaS, Fintech & AI companies. The result? Compounding growth made through organic positioning everywhere people search for information, including both Google & LLM search engines.
