A Knowledge Panel is Google's structured summary of an entity: a company, a person, a product, a place. It appears on the right side of branded search results and is populated from Google's Knowledge Graph, which itself draws from a mixture of sources: Wikipedia, Wikidata, your own structured data, and a range of third-party data providers.
The Knowledge Panel is not your homepage excerpt. It is not a meta description. It is a separate, structured representation of your entity that Google maintains and updates independently of your website. You can influence it (via Wikidata edits, Knowledge Panel claim verification, and ensuring your schema data is consistent) but you cannot directly control it.
Why does this matter for AI search? Because Google AI Overviews draw from the Knowledge Graph when constructing answers about organisations, categories, and products. When a buyer searches "what is the best observability platform for Kubernetes" and Google constructs an AI Overview, the entity data it has about Datadog, Grafana, or your product comes partly from the Knowledge Graph, not just from crawled pages. Similarly, ChatGPT and Claude have entity representations of well-known companies built into their training data, and those representations often trace back to the same reference sources (Wikipedia, Wikidata) that feed the Knowledge Graph.
The practical implication: fixing your Knowledge Panel and Wikidata entry is one of the highest-leverage technical interventions available in both traditional SEO and GEO simultaneously.
Run this audit for your brand and for your two or three closest competitors. The competitor audit is often more revealing than your own. It shows which entity signals your category leaders have built that you haven't.
Step 1: Check what your Knowledge Panel currently shows
Search your brand name in Google. If a Knowledge Panel appears on the right: read every field. Check the industry classification, the founding date, the headquarters, the description text, the products listed, the key people named. Compare each field against your current reality. Note every discrepancy.
If no Knowledge Panel appears: your brand is either not established enough as a Knowledge Graph entity, or the signals that would trigger one are absent. This is itself a gap. It means Google doesn't have enough structured confidence about your entity to surface a panel.
Step 2: Audit your Wikidata entry
Go to wikidata.org and search your company name. If an entry exists, check: instance of (should be "business" or the appropriate industry type), industry, founded date, headquarters location, official website, key executives (linked to their own Wikidata entities where possible). If fields are missing or wrong, they can be edited directly. Wikidata is a public, collaborative database.
If no entry exists: creating one is the single highest-leverage GEO intervention available to most B2B companies. A properly structured Wikidata entry with accurate properties and linked references is the primary signal that triggers Knowledge Panel generation and feeds into the entity representations LLMs build during training.
Step 3: Check Wikipedia
Does a Wikipedia page exist for your company? If yes: does the lede paragraph describe your current product and positioning accurately? Are the sources cited credible and current? If the description is out of date, the fix is editing the page with accurate information and updated references. If no page exists: Wikipedia notability requirements are a real constraint, but if your company has genuine editorial coverage in credible publications, a case can be made. Don't create a promotional page. Wikipedia editors will delete it. Create an encyclopaedic entry with proper citations.
Step 4: Verify your Organisation schema
Your homepage should have an Organisation schema block that includes name, url, logo, description, foundingDate, numberOfEmployees (or a range), address, and sameAs links to your Wikidata entry, LinkedIn page, Crunchbase profile, and Wikipedia page where applicable. The sameAs array is the signal that tells Google's entity resolution system that these different sources are all talking about the same entity. Missing sameAs links are the most common schema gap I find.
JSON-LD · Organisation schema with sameAsRecommended
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your Company",
"url": "https://yourcompany.com",
"logo": "https://yourcompany.com/logo.png",
"description": "One sentence. Specific. Uses the category language buyers search for.",
"foundingDate": "2019",
"address": {
"@type": "PostalAddress",
"addressLocality": "London",
"addressCountry": "GB"
},
"sameAs": [
"https://www.wikidata.org/wiki/Q[YOUR_ENTITY_ID]",
"https://en.wikipedia.org/wiki/Your_Company",
"https://www.linkedin.com/company/yourcompany",
"https://www.crunchbase.com/organization/yourcompany"
]
}
Step 5: Verify description consistency
Pull the description text from: your homepage, your Wikidata entry, your Wikipedia lede (if one exists), your Crunchbase description, and your LinkedIn About section. Are they saying the same thing about your category, your customer type, and your core value proposition? If they diverge (different vocabulary, different category placement, conflicting scope) you have a description coherence problem that will suppress both Knowledge Panel accuracy and LLM entity representation quality. This connects directly to the entity coverage map framework for diagnosing and fixing description gaps.
If your brand has a Knowledge Panel, you can claim it through Google Search Console, which gives you the ability to suggest edits, though Google retains editorial control over what appears. Claiming is worth doing because it gives you visibility into what Google's Knowledge Graph says about you and a formal channel to flag inaccuracies.
More important than claiming, however, is fixing the underlying data sources that feed the panel. Google's Knowledge Graph draws primarily from Wikidata, Wikipedia, and structured data on your own site. Correcting those sources (rather than trying to edit the panel directly) is the more reliable path to accurate, current Knowledge Panel data.
The same principle applies to LLM entity profiles: you can't directly edit what ChatGPT or Claude knows about your brand, but you can ensure that the sources they draw from describe you accurately, consistently, and in the terms your buyers use.