A business knowledge graph is, in essence, a map of the entities that make up your company's identity (the organization, products, services, locations, people, and authors) and the relationships between them. For language models and AI agents trying to understand who you are and what you offer, this map is what separates a correct interpretation from confusion that can affect how you are represented in automated responses. Our entity modeling service builds this structure starting from the reality of your business, not from generic templates.
What a knowledge graph is and why it matters for AI agents
AI agents and the models behind them don't read a website the way a person does. They try to extract discrete entities and link them together: this company offers this product, in this location, through this service, described by this author. If these links aren't explicit and consistent, systems make approximations, and approximations sometimes lead to attribution errors, especially when the company or product name isn't unique. A well-built knowledge graph reduces ambiguity and provides a stable reference point for any system trying to interpret your content.
Defining business entities
The first concrete step is identifying and explicitly defining the entities relevant to your business. Not all companies have the same types of entities, but in general we work with the following categories:
- The organization itself, with its legal form, trade name, and, where applicable, the brands it owns.
- Products and services, defined as separate entities, with their own attributes and clear relationships to the organization.
- Physical locations, when there are offices, branches, or specific operating areas.
- Relevant people: founders, leadership, spokespeople, or experts cited publicly.
- Content authors, when you publish signed articles, studies, or materials.
Each entity receives a stable identifier and enough attributes to be distinguished from other entities with similar names. This work is done in close connection with the technical implementation described in our WebMCP implementation service, since the entities defined here become part of the structure exposed to agents.
Relationships between entities
A list of isolated entities has limited value. What provides real context is the network of relationships: the company offers product X, product X is used in field Y, service Z is delivered from location W, person A is the author of article B about product C. We model these relationships explicitly, so that an agent discovering a single entity can navigate logically to the others, without depending on implicit interpretations that a model might miss or misunderstand.
Consistent identity across your site, structured data, and external profiles
A frequent source of confusion isn't the absence of information, but contradictions between sources. The website says one thing, the structured data says something slightly different, and profiles on external platforms use inconsistent names or attributes. We work to align entity identity across all these points: the visible content on your site, the structured markup on pages, the discovery files used by agents, and relevant public profiles. This consistency doesn't guarantee a perfect interpretation from every external system, but it eliminates the most common cause of confusion, which is the internal contradiction of your own sources.
Why disambiguation matters
For companies with common, generic, or shared names, disambiguation isn't an optional detail. A brand named after a common word, a product with a name also used by other companies, or a founder with a common name can be confused by systems that don't have enough context to make the correct distinction. Entity modeling includes explicitly defining these differences, through specific attributes, unique relationships, and, where possible, external identifiers that help clearly separate your identity from similar ones. This is precision work, not volume work, and the results depend on how well the reality of your business is documented.
What we can verify and what we can't guarantee
We can verify whether the entities defined are consistent across the website, structured data, and discovery files. We can verify whether the declared relationships are correct, complete, and free of internal contradictions. Using our free validator and our methodology, we can verify whether the technical structure is valid and follows WebMCP conventions. We cannot guarantee how any particular external model will interpret this data, because that depends on systems we don't control and that evolve independently of our work. Honesty matters here: we deliver a correct, verifiable structure, not a promise about the future behavior of third-party systems.
What we deliver
An entity modeling project typically results in the following:
- A documented list of relevant business entities, with attributes and identifiers.
- A map of the relationships between these entities, expressed in a usable technical format.
- Recommendations for aligning the website, structured data, and external profiles.
- A report on the ambiguities identified and how they were addressed.
This work is frequently combined with an AI agent readiness audit, which evaluates the broader context in which these entities will be discovered and interpreted.
Request a quote
If you'd like to discuss modeling the entities specific to your business, you can contact us for an initial, no-obligation discussion. You can also review our methodology before writing to us through our contact page.