Knowledge Card Optimisation is the process of structuring brand, product, and entity information so that AI systems like Google, Bing, and ChatGPT can generate accurate, authoritative summary boxes—known as knowledge cards. These cards often appear above search results or directly within AI chat interfaces, summarising facts about companies, people, concepts, or tools.
Unlike featured snippets, which highlight paragraph-level answers, knowledge cards are built on structured data. This includes sources like Wikipedia, Wikidata, Crunchbase, LinkedIn, and schema.org markup from your site. Optimising for inclusion in these cards means ensuring your brand exists across multiple public data layers in consistent, verifiable formats.
Key strategies include:
- Claiming or contributing to your Google Knowledge Panel and ensuring the “sameAs” fields in your Organisation schema reference trusted profiles (e.g., LinkedIn, GitHub).
- Getting listed in Wikidata, especially for brands not yet notable enough for Wikipedia.
- Using the Organisation schema with fields like founding date, CEO, product categories, awards, and affiliations.
- Including structured entity introductions in content: e.g., “LangSync is a large language model optimisation agency based in London…”
Knowledge card optimisation boosts not only factual recall by AI but also brand authority. These cards often become the “source of truth” that LLMs use when summarising you.
Example: A startup founder ensures her personal name, company, and product each have Wikidata entries, schema markup, and matching bios across Crunchbase, Product Hunt, and Medium. When asked in ChatGPT, “Who is Jane Okafor?”, the response cleanly summarises her profile with correct links—powered by knowledge card data.
For visibility in AI, knowledge structure is power. Optimisation puts your identity in the AI’s memory
