How LLMs Choose Which Brands to Recommend

When a user asks ChatGPT "What's the best CRM for small businesses?" the model doesn't pull up a ranking list. It constructs an answer by synthesizing patterns from its training data — billions of text documents, articles, forums, reviews, and documentation pages. The brands that appear in that answer aren't random. They follow a pattern.

Understanding how LLMs select which brands to mention is the first step to ensuring yours is among them.

The Anatomy of an LLM Recommendation

Large language models generate text by predicting the most likely next token (word) given the context. When asked about brand recommendations, the model draws on several types of signals:

Frequency of Mention in Training Data

Brands that appear frequently across diverse, high-quality sources in the model's training data are more likely to be mentioned. This is the most fundamental signal. If your brand is discussed in industry publications, comparison articles, review sites, and authoritative blogs, the model has more material to draw from.

But frequency alone isn't enough — the context of those mentions matters enormously. A brand mentioned 1,000 times in customer complaints will generate different recommendations than one mentioned 1,000 times in positive reviews and expert analyses.

Authority of Sources

Not all sources carry equal weight. LLMs learn patterns from their training data, and content from authoritative sources — industry publications, established media, expert blogs, official documentation — carries stronger signal than content from low-authority sources. This mirrors how humans assess credibility: a recommendation from a respected industry analyst carries more weight than one from an anonymous forum post.

Recency and Retrieval

Modern LLMs increasingly use retrieval-augmented generation (RAG) — searching the web in real-time to supplement their training data. Platforms like Perplexity and ChatGPT with browsing enabled actively retrieve current web content. This means your brand's visibility isn't limited to what was in the training data; fresh, well-optimized content can influence recommendations in real-time.

Structured Data and Clear Formatting

LLMs process text, and text that is clearly structured is easier for them to extract and synthesize. Brands with well-organized content — clear headings, FAQ sections, comparison tables, schema markup — provide the model with easy-to-cite information. If your product page clearly states features, pricing tiers, and use cases in a structured format, the model can more accurately represent your offering.

Why Some Brands Dominate AI Responses

The brands that consistently appear in LLM recommendations share common characteristics:

• Rich, authoritative content. They publish detailed guides, comparisons, and educational content that positions them as thought leaders. This content becomes training data for AI models.
• Widespread third-party mentions. They're discussed in industry publications, review sites, comparison platforms, and expert blogs — not just their own marketing materials.
• Clear product positioning. Their messaging clearly articulates what they do, who it's for, and how they compare to alternatives. Ambiguous positioning leads to ambiguous AI responses.
• Technical optimization. Their websites use structured data (JSON-LD), have clean sitemaps, fast load times, and proper crawlability — making it easy for AI systems to index and understand their content.
• Consistent brand narrative. Across all touchpoints, the brand story is consistent. LLMs synthesize information from many sources; inconsistent messaging creates confused, unreliable recommendations.

The Three Tiers of AI Mention

Not all AI mentions are created equal. At Batwise, we distinguish three tiers of how brands appear in AI responses:

Tier 1: Source Citation

The model references your brand's content as a source but doesn't explicitly recommend you. Example: "According to [Brand]'s research, AI search is growing at 40% year over year." This builds authority but doesn't directly drive consideration.

Tier 2: Comparison Mention

The model includes your brand in a comparison or list. Example: "Popular options include [Brand A], [Brand B], and [Brand C]." This puts you in the consideration set but doesn't distinguish you from competitors.

Tier 3: Explicit Recommendation

The model directly recommends your brand for the user's use case. Example: "For small businesses looking for ease of use, [Brand] is often recommended because..." This is the highest-value mention — it directly influences purchase decisions.

The Batwise Funnel measures your brand's presence across all three tiers, helping you understand not just if you're mentioned, but how you're positioned in AI responses.

What You Can Do About It

Influencing LLM recommendations is not about gaming the system — it's about genuinely making your brand more visible, authoritative, and clearly positioned. Here are actionable strategies:

1. Create citable content. Publish original research, detailed comparisons, and definitive guides in your domain. Content that provides unique data or insights is more likely to be referenced.
2. Build your FAQ page. A comprehensive FAQ with clear, direct answers to common questions is one of the most citable content formats for LLMs. Structure it with FAQPage schema markup.
3. Earn third-party mentions. Get reviewed, get mentioned in industry publications, participate in comparisons. LLMs trust third-party signals more than first-party claims.
4. Implement structured data. Use JSON-LD schemas (Organization, Product, FAQ, HowTo) to make your content machine-readable. This helps both traditional search engines and AI crawlers.
5. Monitor and iterate. Use a platform like Batwise to track how AI models mention your brand, identify gaps, and measure the impact of your optimization efforts over time.

The New Brand Battlefield

The brands that will win in the AI era are those that understand this new landscape and adapt their content strategy accordingly. LLM recommendations are not black boxes — they follow patterns that can be understood, measured, and influenced.

The first step is knowing where you stand. The second is having a systematic approach to improving your position. That's exactly what AI visibility monitoring is designed to do.