{"id":6878,"date":"2026-01-08T11:38:49","date_gmt":"2026-01-08T16:38:49","guid":{"rendered":"https:\/\/beomniscient.com\/?p=6878"},"modified":"2026-01-08T11:40:30","modified_gmt":"2026-01-08T16:40:30","slug":"how-llms-source-brand-information","status":"publish","type":"post","link":"https:\/\/beomniscient.com\/blog\/how-llms-source-brand-information\/","title":{"rendered":"How LLMs Source Brand Information: An Analysis of 23,000+ AI Citations [Research]"},"content":{"rendered":"\n

In 2026, AI engines are a primary interface for customers learning about brands and their products or services.<\/p>\n\n\n\n

From Omniscient\u2019s buyer behavior research<\/a>, we learned 42% of B2B decision-makers use an LLM in the first step of the buying process.<\/p>\n\n\n\n

That comes in many forms: comparing competitors, researching pricing, or evaluating whether a product is worth it. Other times, it\u2019s asking what customers think, whether a brand is trustworthy, or how well a product actually works.<\/p>\n\n\n\n

In those moments, users aren\u2019t always going directly to the source (the brand website) or scrolling review sites; they use an AI engine to synthesize the best available information.<\/p>\n\n\n\n

Marketers must maintain strong brand visibility, brand perception, and ensure accurate information in every place their customers are searching. <\/p>\n\n\n\n

This raises a more uncomfortable question for brands: when AI answers questions about you, whose version of your story is it telling?<\/strong><\/p>\n\n\n\n

In other words, when users explicitly mention a brand in an LLM query, where does the model actually get its information?<\/p>\n\n\n\n

I analyzed 23,000+ citations across hundreds of branded queries to understand which sources LLMs rely on most when citing their answers to brand-specific questions. <\/p>\n\n\n\n

Methodology<\/h2>\n\n\n\n

I used Peec AI<\/a>, an AI visibility platform, to run this analysis. Peec takes prompts of my selection and runs them through multiple AI engines (ChatGPT, Perplexity, Gemini, AI Mode, and AI Overviews), returning both the LLM output and the clickable citation sources referenced in each response.<\/p>\n\n\n\n

I imported 240 unique prompts into Peec, each containing a specific brand name. Because every prompt was queried across multiple AI models and produced multiple citations per output, the dataset totaled 23,387 unique sources.<\/p>\n\n\n\n

The prompts were evenly distributed across:<\/p>\n\n\n\n

Four industries<\/p>\n\n\n\n