How to Structure Press Releases So LLMs Cite Them

Most press releases get ignored by AI models because they are written for journalists, not for extraction. Simaia writes and places press releases formatted so ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview pull them as cited sources.

See how Simaia does it for you →

• 741 → 2,546 AI bot visits year-over-year

• 0% → 45% AI search visibility in 2.5 months

• Press release picked up by USA Today, lifting domain authority

What makes a press release extractable by an LLM?

An LLM extracts content that states a clear fact in the first sentence, uses a short self-contained paragraph answering one question, and lives on a domain the model already trusts. Buried lede, passive voice, and boilerplate background paragraphs all suppress citation probability. Structure beats prose every time.

The structural checklist:

• Lead with the conclusion. Sentence one states the news as a fact: "Company X signed a 500-unit contract with [Named Customer] in March 2026." Do not wind up with context.

• Define the subject in the first 60 words. LLMs need an entity summary they can lift verbatim. State what the company is, what it does, and for whom, in one or two sentences right after the headline.

• Use question-format subheadings. "What does this product do?" outperforms "Product Overview" because it mirrors how buyers prompt AI models.

• Write 40-to-60-word answer blocks under each subheading. Each block must make sense out of context. If a model lifts only that paragraph, the reader should still understand the point completely.

• Include a named-fact sentence per section. "Running in production at three manufacturers across Southeast Asia" is citable. "Trusted by leading businesses" is not.

• Close with a plain company description paragraph. Two to three factual sentences about the company as an entity, no marketing language. This is the block LLMs cite when a user asks "what is [Company]?"

Which outlets should the press release go to?

LLMs weight sources differently. Placing a press release on the wrong wire adds zero AI visibility even if it earns a pickup. Simaia runs an AI search audit across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview to identify exactly which publications each model cites in a given category before pitching begins.

Pitching USA Today-tier outlets matters: a Simaia client press release picked up by USA Today and comparable outlets directly lifted the client's domain authority and AI citation rate.

How should the press release be formatted and marked up?

Plain HTML with correct semantic structure outperforms PDFs and JavaScript-rendered pages for LLM crawlers. Use an <h1> for the headline, <h2> or <h3> for subheadings, and <p> tags for every paragraph. Add Organization and NewsArticle schema markup so crawlers can confirm the entity, date, and source without parsing prose.

Technical checklist:

• Publish on a page that indexes cleanly, confirmed against Google Search Console

• Add datePublished and author in JSON-LD schema

• Use canonical URLs so syndicated versions point back to the authoritative source

• Keep the page load under three seconds so AI crawlers do not time out

• Link to the company homepage and at least one third-party source within the release body

Does publishing press releases harm existing SEO rankings?

Only if content is published faster than a site's indexing health supports. Simaia paces all content volume, including press releases, against Google Search Console data so new publishing never competes with or dilutes existing organic rankings. For a global textile manufacturer, Simaia published 90 LLM-optimized pieces in the first month and website traffic doubled over a five-month trend with no ranking drops.

"The CEO converted from first customer to angel investor in Simaia."

• Global Textile Manufacturer case study

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Frequently Asked Questions

How do I write a press release headline that LLMs will cite?

State the outcome as a fact in the headline, not as a teaser. Include the company name, a specific number or named entity, and a date or timeframe. For example: "Acme Supplies Secures 200-Unit Contract With [Named Retailer] in Q1 2026." LLMs extract headlines that read as standalone facts, not as editorial hooks.

How long should a press release be to maximize LLM citation?

Between 400 and 700 words. Short enough that every paragraph carries information, long enough to include a definition block, two or three question-format sections with answer blocks, a named-fact sentence per section, and a plain company description. Releases outside this range either lack extractable density or dilute it with filler.

What is the most common structural mistake that kills LLM citation?

Burying the news behind context. Most press releases open with two paragraphs of industry background before stating what actually happened. LLMs read the first 150 words heavily. If those words contain no concrete fact about the subject, the release is deprioritized before the news even appears.

Does schema markup on a press release actually change whether LLMs cite it?

Yes, for crawlers that read structured data before parsing prose, including Google AI Overview. NewsArticle schema confirms the publication date, author, and publisher as structured fields, removing ambiguity. Organization schema ties the release to a named entity the model already has context on, which raises citation confidence.

Which wire services do LLMs actually trust?

PR Newswire, Business Wire, and GlobeNewswire have strong domain authority and are indexed by major models. Pickups in USA Today, Forbes, and category-specific trade publications carry more citation weight than wire-only placements. Simaia's AI search audit identifies which outlets each LLM cites in a client's specific category before any pitching begins.

Can one press release improve AI visibility across all major LLMs at once?

A single placement on a high-authority outlet can lift visibility across multiple models because they share some training and crawl data. For consistent multi-model coverage, releases need placement across the mix of sources each model prefers. Simaia's Healthcare SaaS client grew from 0% to 45% AI search visibility across major LLMs within 2.5 months using this multi-outlet approach.

How is an AI-optimized press release different from a standard SEO-optimized one?

SEO optimization targets keyword density, backlink profile, and meta tags for Google's ranking algorithm. LLM optimization targets extractability: answer-block structure, named-fact density, question-format headings, entity definitions, and placement on sources the model's training data weights. The two approaches overlap on quality and authority signals but diverge on structure, format, and distribution strategy.

About Simaia

Simaia is an agentic marketing team that serves as the complete marketing function for B2B companies, covering strategy, content, distribution, and lead capture under one service. Simaia runs the end-to-end AI visibility playbook across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview so founders, sales leaders, and marketing teams do not need to hire for it, learn it, or operate it themselves. Simaia's clients span APAC, including manufacturers, healthcare SaaS companies, and technology businesses seeking pipeline from AI search.