How to Improve Your AI Visibility (and What a 'Good' Score Looks Like)

You ran an audit, got a score back, and now you're trying to work out whether it's good, bad, or meaningless. Or maybe you skipped the score entirely — you just noticed that when you ask ChatGPT or Perplexity to recommend a tool in your category, your brand never comes up, the exact pattern covered in why your brand is invisible to AI. Either way, you want two things: a real plan for moving the number, and an honest read on how much that number should actually worry you.

How do you improve your AI visibility?

You improve AI visibility by being the clearest answer to buyer questions, earning third-party mentions on the sources AI models trust, and fixing the technical signals that let engines quote you accurately. Every tactic that actually works rolls up into one of those three pillars.

• Content — pages that directly answer the questions buyers type or speak into an AI engine, not pages that describe your product to your product.
• Consensus — reviews, comparisons, and forum threads written by other people about you, which carry far more weight than your own copy ever will.
• Signals — schema markup, consistent entity naming, and a site engines can actually crawl and parse without tripping over it.

Most teams over-invest in content and under-invest in the other two, then can't understand why a page they consider "perfectly optimized" never gets cited. AI engines don't just read your page — they cross-check it against what everyone else says about you, and whether they can technically trust the source in the first place. Content proves you understand the question. Consensus proves other people agree. Signals prove the engine can verify both without guessing.

The 6 levers that actually move AI visibility

Six things reliably move AI visibility: answer-shaped content, off-site consensus, entity clarity, freshness, topical authority, and technical crawlability. The large language models behind engines like ChatGPT, Perplexity, and Google AI Overviews weigh all six when deciding who to cite, so starving any single one puts a ceiling on what the other five can do for you.

1. Answer-shaped content. Pages written as a direct, specific answer to one buyer question get quoted; pages written as brand narrative almost never do. Structure each page around a single question, answer it in the opening two sentences, then back it up with concrete specifics instead of adjectives like "leading" or "innovative."
2. Off-site consensus. AI engines weight what other people say about you far more heavily than what you say about yourself, because third-party mentions read as independent evidence rather than marketing copy. Prioritize getting listed and reviewed on the sites your buyers already trust — G2 or Capterra for software, relevant Reddit threads, independent "best X" roundups — instead of only polishing your own site.
3. Entity clarity and schema. If an engine can't confidently work out what you are, who you compete with, and which category you belong to, it has no reason to risk naming you. Use identical naming everywhere your brand appears online, add Organization and Product schema markup, and keep your "what we do" line word-for-word consistent across every page.
4. Freshness. Engines lean toward recently updated, clearly dated content, because a stale page implies the information — and possibly the product — might be out of date. Put a visible last-updated date on your key pages and revisit your highest-traffic content on a regular cadence instead of publishing once and never returning.
5. Topical authority. A single strong page rarely earns citations on its own; engines trust brands that visibly cover an entire topic, not just one keyword. Build a cluster of genuinely related pages around your core subject and interlink them so the depth is obvious to both readers and crawlers.
6. Technical crawlability. None of the previous five levers matter if engines can't fetch or parse your pages in the first place. Confirm robots.txt and llms.txt aren't blocking AI crawlers, keep essential content server-rendered rather than client-side-only JavaScript, and check that bots see real content instead of a paywall or a blank shell.

Here's the same six levers mapped to what each one actually proves to an engine:

None of these levers is optional, and none is a quick fix. Brands that treat AI visibility like a technical SEO checklist usually stall out at entity clarity and never get to consensus — which is where most of the real movement actually happens.

What is a good AI visibility score?

There's no universal, industry-standard scale for AI visibility — a "good" score depends entirely on which tool produced it and how that tool weights its checks. In practice, "good" means being named in the majority of your category's high-intent buyer questions across the major engines, not hitting some specific number on somebody's dashboard.

That's worth being blunt about. A high score from one tool doesn't mean the same thing as a high score from another. Tools differ in which engines they poll, how many prompts they sample, whether a passing mention counts the same as a first-place recommendation, and how heavily they weight sentiment. A score is a snapshot built on that tool's own rubric — genuinely useful for tracking your own trend over time, close to meaningless for comparing yourself against a competitor's number from a different tool. That distinction matters, because a scary-looking score can send a team into panic mode over nothing, and a reassuring one can just as easily paper over a real gap.

So instead of chasing a universal number:

• Track your own score, from one consistent tool, over time — the direction matters far more than the digit.
• Manually run a handful of the real questions a buyer would ask — "best [category] for [use case]," "[you] vs [competitor]" — across ChatGPT, Perplexity, and Google AI Overviews, and count how often you actually show up.
• Treat a rising mention rate in those real buyer questions as the actual win condition. The score is a proxy for that; it isn't the goal itself.

How to measure and track it

Measure AI visibility by running the same set of category buyer questions across the major engines on a recurring cadence, then tracking whether you're named — not just whether you rank in a traditional search result. A single check tells you where you stand today; only repeated measurement tells you whether the six levers above are actually working.

Our guide to measuring AI visibility walks through building that question set and reading the results engine by engine, including what to do when different engines disagree about you. AEOeye automates the process end to end: it runs your brand through ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude, then scores you across all of them so you're not manually copy-pasting prompts every week. You can look at an example report before running your own, to see exactly what gets measured and how it's presented. Monthly is a reasonable cadence for most brands; weekly if you're actively working through the six levers and want to see movement in near real time.

Improving your score is really just improving the odds that an AI engine, faced with a real buyer question, reaches for your brand instead of a competitor's. Get the six levers right and the number tends to follow on its own. Chase the number directly, and you'll spend a lot of effort optimizing for a scale nobody else is using.

If you just want a quick, no-signup read first, start with a free AI visibility checker. Ready for the full picture? Get your AI visibility score in under a minute — free. Run a free AI visibility audit.