In search, an entity is a well-defined thing that can be uniquely identified: a company, a product, a person, a place or a concept. Search engines store entities and the relationships between them, which lets them understand that “Quillbrook” is a software company, that it makes invoicing software, and that it integrates with Stripe. Language models also learn associations between entities from the text they are trained on. Being a clear, consistently described entity makes a brand easier to recognize and recommend.
Entities versus keywords
A keyword is a string of words. An entity is the thing the words refer to. “Apple” the company and “apple” the fruit share a keyword but are different entities. Search systems that understand entities can connect a query to the right thing even when the wording varies, and can answer questions about relationships, such as which tools integrate with a given platform.
Making your brand a clear entity
For a SaaS company, the aim is that every source describes the brand the same way, so systems can join the dots.
- Use one consistent name, description and category across your site, profiles and listings.
- Add Organization structured data on your homepage, with your logo, URL and sameAs links to official profiles.
- Keep profiles current on review sites, marketplaces and directories relevant to your category.
- State relationships plainly: what you integrate with, who you serve, what category you belong to.
- If your name is ambiguous, pair it with the category in copy, such as “Quillbrook invoicing software”.
Organization markup
A minimal example. Only include properties that are true and visible elsewhere on the site.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Quillbrook",
"url": "https://ledgerly.example",
"logo": "https://ledgerly.example/logo.png",
"description": "Invoicing and expense software for small agencies.",
"sameAs": [
"https://www.linkedin.com/company/ledgerly-example",
"https://github.com/ledgerly-example"
]
}
</script>Example
A fictional brand with a common name
Beacon, a made-up customer success platform, shares its name with dozens of other products. AI answers about “Beacon pricing” sometimes describe a different company. The team adds Organization markup with sameAs links, makes its homepage title and first sentence say “Beacon customer success platform”, and corrects its descriptions on review sites so they match. Those steps give systems more consistent signals; they do not guarantee every answer gets it right.
Common mistakes
Inconsistent descriptions
Calling yourself a CRM on one profile and a sales engagement tool on another muddies which category you belong to.
Markup that contradicts the page
Structured data should match visible content. Mismatches are ignored at best.
Questions
No. Wikipedia and Wikidata are strong sources, but they have strict notability rules, and most SaaS companies build entity signals through their own site, structured data and third-party profiles.
A knowledge panel for a brand search is one sign. Google's Knowledge Graph Search API can also return entity records, though it does not show everything Google holds.
