This is how AI seesyou.

AI visibility is how often, how accurately, and how favorably AI assistants — ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, and Bing — name your brand when a buyer asks a question in your category. Classic SEO measures where your page ranks on a search-results page; AI visibility measures whether you appear inside the model's generated answer at all. The ranking is no longer a list of ten blue links a user scans — it is a single synthesized response that may name two or three brands and cite a handful of URLs. If you are not in that answer, the buyer never sees you, regardless of your Google rank.

When an AI assistant answers a question it often grounds its response in sources and links them as citations. Those citations are the modern equivalent of a top organic result: they are the pages the model trusts enough to point a user toward, and they disproportionately shape which brands get named. Being cited does three things — it puts your name in the answer, it sends referral traffic from the assistant's UI, and it teaches the model on future runs that your page is an authoritative source for that topic. Lumear tracks every citation across every answer so you can see which of your pages earn them, which competitor pages are taking your place, and which high-intent questions cite nobody yet (the openings worth winning).

Two mechanisms. First, parametric memory: facts absorbed during training, which is why well-documented, widely-referenced brands surface even without live retrieval. Second, retrieval: for current or specific questions the model runs a live search, reads the top pages, and grounds its answer in what it finds — so fresh, well-structured, clearly-attributed content can get cited even by a brand the model had never "memorized." In practice the brands that win are the ones whose pages are crawlable, answer the question directly in plain language, state claims a model can quote with confidence, and are corroborated across multiple independent sources. Lumear reverse-engineers this by running the real questions your buyers ask, capturing exactly which sources each model pulled, and showing the gap between what was cited and the page on your site that should have been.

JSON-LD (JSON for Linking Data) is a lightweight format for embedding machine-readable facts in a web page using the shared schema.org vocabulary. You drop a <script type="application/ld+json"> block in the page describing what it is — an Organization, a Product with its price, an Article with its author and date, a FAQPage with its questions — in unambiguous key/value form. Instead of guessing your pricing from prose or your author from a byline, a crawler reads it directly. It is the same standard that powers Google's rich results, and it is increasingly how AI systems extract reliable entity facts about a brand.

Structured data removes ambiguity. An AI system extracting facts from prose has to infer your company name, product, price, and key claims — and inference is where errors and omissions creep in. JSON-LD states those facts explicitly, so the model extracts them on the first pass with high confidence and is more likely to reproduce them accurately when it answers. Schema also makes a page eligible for richer treatment (FAQ answers, product offers, breadcrumbs) and signals topical structure that helps a model decide a page is the authoritative answer to a specific question. It is not a magic ranking lever — content still has to be good — but it meaningfully raises the odds your facts get cited correctly rather than approximated or skipped.

Content Studio turns a visibility gap into a finished, citation-ready asset. When Lumear finds a question where you are mentioned-but-not-recommended, or not mentioned at all, Content Studio drafts an article grounded in your own evidence — the real prompts, the competitor citations that beat you, and the claims the model is rewarding. Every draft is written in the plain, directly-answering style assistants prefer, anchors its claims to sources, and ships with ready-to-paste schema.org markup (FAQPage, Article) so the page is structured for extraction the moment it goes live. The goal is not generic blog filler; it is the specific page that should have been cited, built to win the next run.

After a run, Lumear parses every AI response for brand mentions, sentiment, citations, and competitive framing, then matches each answer against a fresh crawl of your site and your competitors'. From that it computes the highest-leverage fixes — for example: a question where a competitor page is cited and you have no equivalent page; a page you own that is close to being cited but missing a direct answer or a key claim; or a factual error a model is repeating about you. Each recommendation names the specific question, the specific page, and the specific change, and is ranked by expected impact so you work the gaps that move your visibility most, not a generic checklist.

You re-run the same prompt set on a schedule and watch the metrics move. Lumear tracks your visibility score (how often you appear), share of voice versus competitors, sentiment, and citation count over time, and ties changes back to the recommendations you shipped — so you can see that the page you published for a given question is now the one getting cited. Because the prompt set is held constant, the comparison is apples-to-apples across weeks: a rising line is real lift, not measurement noise. Business and Agency plans add explicit lift tracking that attributes movement to the specific content changes that caused it.