Published August 26, 2026
· Updated September 7, 2026
Focused specifically on chat-based assistants - ChatGPT, Claude, Gemini - as distinct from search-style AI Overviews. These platforms behave differently: some default to training data unless search is explicitly invoked, and each has its own crawler with its own access rules.
We track and optimize for each platform individually, not as a single undifferentiated 'AI search' target.
What This Actually Means, Precisely
LLM Visibility Optimization is the broadest of the three related terms: it covers whether a large language model has any real awareness of your business at all, through any pathway - live web retrieval when a platform searches in response to a query, or the model's own training data if your business had a genuine, indexable web presence before that model's training cutoff. A business can be completely absent from an AI's knowledge in ways that live-retrieval-focused AEO work alone won't fully address.
This service audits both pathways: whether your business is genuinely retrievable right now, and whether it has any real presence a future model's training process would actually pick up on.
How This Differs From AEO and GEO
Think of these three as concentric circles. GEO is the narrowest - the specific writing techniques that help already-retrieved content get selected and cited. AEO is broader - the full process of becoming genuinely citable, from entity consistency to structured data to content quality. LLM Visibility is the broadest - it asks whether the model has any real awareness of you at all, independent of any single query or retrieval event. If your business is being actively researched, cited, and discussed across the genuine, indexable web, that visibility compounds over time into a model's actual training-data awareness, not just its live-search results.
Who This Is Actually For
This fits a business genuinely curious whether AI models "know about" them at all, independent of any specific live search — often a business that's been operating for years with strong offline reputation but thin, recent digital content, or a newer business wanting to understand its baseline before investing further. It's a diagnostic-first service more than an execution-first one, since the honest answer to "what does a model already know about us" often shapes everything else more usefully than jumping straight to fixes.
How We Actually Test This
We query each of the six platforms with training-data-leaning prompts — questions that don't require live retrieval to answer if the model genuinely has relevant background knowledge — alongside the standard live-search queries used elsewhere on this site. The gap between what a model can say about you without searching versus what it finds when it does search reveals two genuinely different things: existing awareness versus current discoverability.
For DeepSeek specifically, this distinction matters more directly than for the other five platforms, since it currently leans more heavily on training data than live retrieval — making genuine, sustained web presence (real coverage, real citations from other sites, real indexed content) the more direct lever for that platform specifically.
What Realistic Progress Looks Like
Being honest about pacing: training-data awareness genuinely can't be fixed on the same 60-90 day timeline as live-retrieval citation, since it depends on a model's next training cutoff, which is outside anyone's control. What we can do now is build the genuine, indexed, cited web presence that improves the odds for the next training cycle — the same real work (structured data, genuine press coverage, consistent entity information) that also improves live retrieval today.
Frequently asked questions
Do all LLMs need the same technical fixes?
No - blocking OAI-SearchBot affects ChatGPT specifically without touching Claude or Perplexity, which is exactly why treating this as one undifferentiated target misses real, platform-specific problems.
How do you measure whether this is actually working?
The same evidence standard as everything else we do: real, stored AI platform responses, checked before and after, not a self-reported summary. You can see the actual answers, not just a derived score.
Is this a one-time project or ongoing work?
Ongoing, by design - AI platforms re-crawl and re-evaluate sources continuously, and a fix that works today can be overtaken by a competitor's fresher content next quarter.
Can a business be invisible to an AI's training data even with an active website?
Yes - if the site is new, poorly indexed, or was launched after a model's training cutoff, it may have no meaningful training-data presence yet, even while being live and technically crawlable now.
Does this mean we need our content republished elsewhere to be included in future training?
Not republished, but genuinely indexed and referenced - being cited, linked to, and discussed by other real, crawled sources increases the odds your business gets meaningful training-data representation over time.
How is training-data visibility different from live retrieval visibility?
Live retrieval happens per-query, in real time, and can be influenced relatively quickly. Training-data visibility is baked into a model at training time and can't be changed retroactively - only influenced for the model's next training cycle.
Does this service focus more on training-data visibility or live retrieval?
Both, genuinely - most real client value comes from live retrieval work (which is faster to influence), but understanding training-data gaps matters for a complete picture of your actual AI visibility.
Can we know for certain what a specific model's training data includes?
Not with certainty - training data composition isn't fully public, but genuine, sustained real-world web presence (citations, coverage, indexed content) is the best available proxy for improving future training-data inclusion.
Does DeepSeek's lack of live search make LLM visibility more important for it specifically?
Yes, directly - since DeepSeek currently relies more heavily on training data than live retrieval, genuine training-data presence matters more for being cited by that specific platform than for the live-retrieval-focused platforms.
How long does it take for new content to meaningfully affect training-data visibility?
This depends on factors outside anyone's direct control - a model's next training cutoff and how thoroughly it crawls the web - so this is inherently a longer-term, less immediately measurable effort than live retrieval work.
Does this service overlap with traditional PR and media coverage work?
Meaningfully, yes - genuine press coverage and third-party mentions are exactly the kind of real, indexed, cited content that improves both live retrieval and eventual training-data representation.
How do you measure current LLM visibility if it's partly about training data we can't directly test?
Through real, systematic testing across all six platforms for a range of query types - what a model can currently answer about your business, even without live search, reveals genuine training-data awareness.
Is this relevant for a genuinely new business with no prior digital history?
Yes, and arguably more urgent - a new business has a real opportunity to build both live-retrieval and training-data presence deliberately, rather than untangling years of inconsistent prior content.
Do you provide this work in Arabic as well as English?
Full, genuine bilingual delivery - real Arabic content written natively, not translated, evaluated and measured separately in each language, and genuinely indexed content in both languages improves visibility for both.
What's the first deliverable in an LLM Visibility engagement?
A real, evidenced baseline across all six AI platforms, including testing what each model currently knows about your business without live search enabled where possible, distinguishing training-data awareness from live retrieval.