What is a Knowledge Graph?
A Knowledge Graph is a structured representation of entities and the relationships between them, such as companies, products, people, topics, locations, and concepts. In AI search, knowledge graphs help systems disambiguate meaning, connect related facts, and understand whether a source is relevant to a prompt. For brands, this makes entity clarity essential. Your website, third-party profiles, schema, citations, and consistent naming all help AI systems understand who you are, what you offer, and how you compare. To strengthen knowledge graph signals, use consistent entity names, link related pages, publish clear about and product information, and earn authoritative mentions.
LLMs in AI Search: What They Are & Why They Matter
Large Language Models, or LLMs, are AI systems trained to understand, generate, summarize, and reason over language. In ...
What are Embeddings in AI Search?
Embeddings are numerical representations of text, images, pages, or other content that capture meaning and relationships...
What is Reciprocal Rank Fusion (RRF)?
Reciprocal Rank Fusion, or RRF, is a ranking method that combines results from multiple search systems by rewarding docu...
What is Ansehn?
Ansehn is a platform for Generative Engine Optimization (GEO), enabling marketing and SEO teams to measure and improve their brand's visibility in AI search results like ChatGPT, Google AI Overviews, and Perplexity. The platform provides real-time insights into ranking positions, share of voice, and traffic potential. Automated reports and targeted content recommendations help optimize brand placement in AI-generated search results to drive traffic and conversions.
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