Can Five Blocks change what AI says about a brand?

Yes. An AI model builds its understanding of a topic from two kinds of sources: the corpus it was trained on, and the live content it pulls in at query time through RAG (Retrieval-Augmented Generation). Five Blocks works on both by making sure the sources behind a brand are present, accurate, and consistent.

How AI forms its understanding of a brand

Major AI engines generate answers about an entity in two ways that work together:

• Training data, the corpus a model was built on (web pages, news, books, Wikipedia, and structured datasets), fixed at the model’s training cutoff.
• Real-time retrieval (RAG), live web content the engine fetches at query time rather than relying solely on its training set. Engines such as Perplexity, ChatGPT Search, and Google AI Overviews use retrieval heavily.

The sources we strengthen

When an AI engine answers a question about a company, it draws on sources from across that company’s digital footprint. Five Blocks focuses on making each of these sources present and reliable:

• Wikipedia, treated by major engines as a foundational reference for entity questions, drawn on from both training and retrieval.
• Owned web properties, the brand’s own sites, ideally with schema markup and clear authorship.
• Authoritative third-party coverage, earned media that engines cite as sources.
• Structured data, schema markup and entity links that help engines identify and connect the entity correctly.
• Wikidata, the structured-knowledge hub that links entities across languages and feeds knowledge-graph systems.

Why this changes what AI says

Properly anchored content can enter the source pools AI engines draw on, which in turn shapes their outputs. By strengthening the quality and consistency of these underlying sources, Five Blocks aims to improve how AI platforms represent clients over time, both the accuracy of the facts and the sentiment of the framing.