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AI HALO

Learn · The boardroom case

A press release built for fact extraction gets quoted correctly within hours, not buried for months.

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Optimizing Corporate Media Releases for Immediate Real-Time LLM Fact Extraction

Traditional press releases are written for human journalists and bury the material fact — a launch date, a figure, an executive quote — inside narrative paragraphs several sentences deep. Real-time-connected assistants like Perplexity and Gemini extract facts more reliably when they're stated plainly near the top and reinforced with NewsArticle or PressRelease schema carrying explicit datePublished, headline, and about fields. Without that structure, a model pulling live results may misattribute a quote, use a stale figure from a competitor's coverage, or omit the release from an answer entirely because it couldn't confidently parse who said what. Optimizing for extraction means writing the lead sentence as a standalone, unambiguous fact, tagging entities and figures explicitly, and ensuring the page itself isn't blocked from the crawlers that power real-time answers. Done correctly, a release becomes citable within the same news cycle an assistant is asked about it, rather than competing for attention weeks later.

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Questions

Answered.

Why would an AI assistant misquote a correctly written press release?+

If the material fact is buried in paragraph three or requires inferring context from earlier sentences, extraction models may grab a nearby but incorrect figure or attribute a quote to the wrong speaker rather than parsing the full narrative.

Does adding NewsArticle schema really change what a real-time model shows?+

Yes — schema gives the model explicit, labelled fields for headline, date, and entities involved, removing the ambiguity that causes it to either skip the source or default to a secondary outlet's paraphrase instead.

Should the release still read naturally for human reporters?+

Yes; extraction-friendly structure and readable journalism aren't in conflict — a clear, front-loaded lead sentence with supporting schema serves both a human reporter skimming for the story and a model parsing for facts.

Proof & data

Most AI-visibility tools only watch — they report where you are absent and stop there. AI HALO does the work that changes the answer, then re-scans to prove it.

$29–$780/mo
what monitoring tools charge to report your AI visibility
$1,500–$50k/mo
what GEO agencies charge to execute — ongoing retainer
One investment
what AI HALO asks to do the work + a 30-day proof re-scan

Measured live across ChatGPT · Claude · Gemini · Meta AI · Grok · DeepSeek — we ask the models your buyers’ real questions, before and after.

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