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AI HALO

Learn · The mechanics of AI visibility

AI answers about your business are often built live from whichever sources retrieval ranks highest.

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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.

The RAG Paradigm: How Retrieval-Augmented Generation Selects Sources

Retrieval-augmented generation is the process behind many real-time AI answers: rather than relying purely on what a model learned during training, the system first retrieves a small set of live documents relevant to the question, then generates a response grounded in that retrieved content. Which documents get retrieved is governed by a separate ranking layer that behaves much like a search engine — weighing relevance, freshness, structural clarity, and domain authority before the language model ever sees the text. This means a business can be well known yet still invisible in AI answers if its content isn't structured for retrieval: pages needs clean, extractable passages, current information, unambiguous entity naming, and technical accessibility to the crawlers feeding that retrieval index. Optimizing for RAG means treating the retrieval layer as a gatekeeper distinct from the generation layer, and giving it the clearest possible passage to select when a buyer's question comes in.

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Questions

Answered.

Is RAG the same mechanism across ChatGPT, Gemini, and Perplexity?+

No. Each assistant uses its own retrieval pipeline and ranking signals, though they share common principles like favoring structured, current, and crawlable content. Optimizing broadly for clarity and freshness improves odds across all of them rather than gaming one specifically.

Can a page rank well in Google but still be skipped by RAG retrieval?+

Yes. Traditional search ranking and AI retrieval ranking use overlapping but distinct signals, and RAG systems often weight passage-level clarity and recency more heavily, so a page can perform well in one system and poorly in the other.

Does page freshness actually affect whether RAG retrieves a source?+

Generally, yes. Many retrieval systems apply a recency weighting, particularly for time-sensitive queries like pricing or availability, so pages with stale content are less likely to be selected even if their subject matter is otherwise relevant.

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