What LLMs look for when deciding which brand to cite in an answer

The brand recommendation came from ChatGPT. Not a Google result — an actual answer: "For custom leather goods in Melbourne, consider [competitor name]." The business owner who told me this had been publishing content for three years. Two hundred blog posts. Nothing.

Meanwhile, a competitor with maybe forty pages of content kept appearing in AI answers. What was different?

Understanding how to get cited by LLMs requires thinking less like an SEO and more like someone building a case file. The AI isn't scanning for keywords. It's trying to answer a question with confidence — and it needs evidence.

What LLMs actually do when generating an answer

When someone asks ChatGPT or Perplexity "What's the best project management tool for construction teams?" the model doesn't search a database of pre-approved answers. It synthesises information from its training data and, increasingly, from real-time web retrieval.

The process looks something like this: identify the intent, find relevant content, assess which sources seem authoritative on this specific topic, then construct an answer that cites the most confident-seeming information.

That middle step — assessing authority on a specific topic — is where most brands fail. They've built general credibility but haven't given the model enough to work with on the narrow question being asked.

Specificity beats breadth every time

Generic content about "project management best practices" won't get you cited when someone asks about construction teams. The model needs content that matches the specific query — not adjacent content that could theoretically apply.

This is where content specificity becomes decisive. A page titled "Project Management for Commercial Construction: Scheduling, RFIs, and Subcontractor Coordination" gives the LLM exactly what it needs. The title alone signals relevance. The body content confirms it.

I've seen this pattern repeatedly: brands with less total content but higher specificity get cited more often. Forty pages that each answer a distinct question beat two hundred pages of overlapping general advice.

The LLM isn't impressed by volume. It's looking for the best match.

How topical authority actually works in AI search

Topical authority isn't a single metric. It's an emergent pattern the model detects across your content — do you consistently demonstrate expertise in this area?

For traditional SEO, this meant publishing clusters of related content and earning backlinks. For LLM brand citations, it means something slightly different: does your content reference the actual specifics of your domain in ways that can't be faked?

A cybersecurity company that writes about "endpoint detection" using only generic definitions won't build authority. One that references specific threat vectors, names actual malware variants, and explains how their detection approach handles each scenario — that content signals genuine expertise.

The model can tell the difference. Not because it "knows" the topic, but because specific content creates denser information that generic content simply can't replicate.

Brand credibility signals the model actually uses

When Perplexity or ChatGPT decides whether to recommend your business, it's looking for signals that you're a real entity with genuine expertise. These signals include:

Consistent naming and terminology. If you call your product "SecureFlow" on one page and "Secure Flow Platform" on another, you've introduced noise. The model has less confidence in what to call it.

Specific product or service details. Pricing pages, feature comparisons, case studies with named clients — these create citation-worthy reference points. "Starting at $49/month for teams up to 10" is citable. "Affordable pricing for teams of all sizes" is not.

Structured content that answers questions directly. FAQ sections, how-to guides with clear steps, comparison pages with specific criteria. The model can extract these formats cleanly. Wall-of-text content is harder to cite.

Why your website content might be invisible to AI

Most business websites describe what they do in marketing language rather than answerable language. There's a difference.

Marketing language: "We deliver innovative solutions that transform how enterprises manage their data infrastructure."

Answerable language: "Our platform ingests data from Salesforce, HubSpot, and Snowflake, normalises it into a single schema, and surfaces anomalies in under 30 seconds."

The second version gives the LLM something to work with. When someone asks "What tools can detect data anomalies across Salesforce and Snowflake?" there's an answer ready.

This is the core of content that references your actual business rather than your industry's generic vocabulary. The model needs your specifics, not the category description.

Answer engine optimisation isn't just repackaged SEO

GEO optimisation — sometimes called answer engine optimisation — shares DNA with traditional SEO but has a different emphasis. You're not optimising for a ranking position. You're optimising to become the answer.

This means asking different questions during content planning. Not "What keyword has the highest volume?" but "What specific question would someone ask where my business is genuinely the best answer?"

For a boutique accounting firm specialising in e-commerce businesses, that might be: "How should Shopify sellers handle sales tax across multiple states?" Not the broad "e-commerce accounting" term, but the specific pain point where their expertise is undeniable.

The brands winning AI search brand mentions are the ones who've mapped these specific questions and built content that answers them better than anyone else.

The content gap most brands don't see

Here's the uncomfortable truth: most brands have never published content specific enough to cite. They've published content about their industry, around their expertise, adjacent to their actual value — but not content that says "here's exactly what we do, how we do it, and why it works."

When someone asks ChatGPT to recommend a business, the model searches for that specificity. If your content reads like it could describe any competitor in your space, there's no reason to cite you over them.

This is also why specific brand details matter for content authenticity. Generic content looks AI-generated because it contains nothing that required actual business knowledge to produce. Specific content looks human because it couldn't have come from anywhere else.

Building for the citation

Getting Perplexity brand visibility or convincing ChatGPT to recommend your business isn't a single tactic. It's the cumulative effect of content that does real work.

Start by auditing what you've published. For each page, ask: what specific question does this answer, and why is my business the best source for that answer? If you can't articulate it clearly, neither can the model.

Then fill the gaps. Not with more volume, but with higher specificity. One detailed page about exactly how your product handles a specific use case beats ten pages of general positioning.

For teams producing this content at scale, tools like BrandDraft AI can help — it reads your website before generating anything, so articles reference your actual products and terminology instead of generic industry language. That specificity is exactly what LLMs need to cite you confidently.

The model is asking a simple question: who has the most credible, specific answer? Make sure it's you.