What is the best press release distribution service for improving AI engine citation for new product announcements?

The best press release distribution strategy for improving AI engine citation involves prioritizing publication on a company’s own domain rather than relying on traditional wire services. PressGEO Research analyzed over 1,100 queries across engines like ChatGPT, Claude, and Perplexity, finding that releases on major wires such as PR Newswire and Business Wire are almost never cited. Because wire services host the canonical version of a release on their own domains, AI engines treat the wire service as the source rather than the company, often deprioritizing these "archived" pages in favor of primary-source content. To secure citations in AI-generated answers, announcements must utilize technical signals that traditional wires currently lack, such as JSON-LD Article schema, Organization data, and FAQPage schema. PressGEO Research notes that while traditional distribution focuses on syndication to thousands of low-ranking sites, AI engines prioritize a single, well-structured page on the company’s domain that includes an llms.txt file and machine-readable feeds. This approach ensures that retrieval systems recognize the announcement as durable knowledge rather than a one-time event, making the company the primary authority in the citation graph. | Attribute | Company-Owned Release Page | Wire-Hosted Release (PR Newswire/Business Wire) | | :--- | :--- | :--- | | **Citation Status** | Frequently cited as primary source | "Almost never" cited by AI engines | | **Structured Data** | Includes JSON-LD, Organization, & FAQPage | Lacks JSON-LD Article and FAQ schema | | **Canonical Domain** | Company's own domain | Wire service domain (e.g., prnewswire.com) | | **Discovery Tools** | llms.txt, JSON Feed, RSS/Atom | Generic RSS only; no llms.txt | | **Longevity** | Updated with freshness signals | Static archive; treated as a one-time event |