How to improve brand visibility in AI search engines

A measurement loop, not a tip list: baseline, diagnose the gap, fix it, re-measure.

Key takeaways

• Improving brand visibility in AI search engines starts with a baseline, because the fix depends on which of three gaps you have: never named, named late, or never linked.
• Myrah scores each engine on Presence at 55%, Prominence at 30%, and Citation at 15%. A change that fixes Presence rarely fixes Citation.
• Search visibility, model training, and user-triggered retrieval are separate crawler controls with separate user agents per vendor.
• Google LLC states that llms.txt, artificial chunking, AI-only rewrites, and special AI schema are not requirements for its AI features.
• Re-measure on a fixed cadence with the same prompts, because model output varies run to run and one before-and-after reads as noise.

Introduction

To improve brand visibility in AI search engines, measure how often assistants name you today, work out which of three gaps you have, apply the matching fix, then re-run the same prompts on a schedule. Most teams skip to tactics and never learn which change worked. AI visibility optimization is a loop: baseline, diagnose, fix, re-measure.

It is for marketing leads and SEO managers at SaaS companies, ecommerce brands, and agencies already running traditional SEO who are now asked why ChatGPT recommends a competitor. For the definition see what AI visibility means; for scoring formulas and sampling math see how brands measure visibility in AI.

AI brand visibility is how often and how favourably AI assistants name, rank, describe, and cite your brand when someone asks a question in your category without naming you. It matters because the answer replaces the click: your buyer reads a recommendation, not a results page.

What strategies improve brand visibility in AI search engines?

Six strategies do the work, in the order you should run them.

1. Baseline with unbranded prompts. Ask what buyers ask, never "tell me about [your brand]". A branded prompt guarantees a mention and measures nothing.
2. Diagnose the gap type. Presence, prominence, and citation gaps have different causes and fixes.
3. Fix crawler access for the right bot. Search crawlers, training crawlers, and user-triggered fetchers are separate controls.
4. Make pages liftable. Direct answers near the top, clean headings, FAQ blocks, schema, one topic per page.
5. Earn third-party mentions. Comparison pages, listicles, and threads feed synthesis you cannot edit.
6. Re-measure on a fixed cadence. Same prompts, same engines, same schedule.

Baseline before you change anything

AI answers vary run to run, so a baseline is a fixed prompt set, run against multiple engines and stored so you can repeat it identically.

Prerequisites before step 1: a list of direct competitors, the three to five buying questions your sales team hears most, and a decision about which engines matter to you.

1. Build the prompt set. Myrah organizes prompts as Category ("best inventory software for multichannel sellers"), Compare ("X vs Y for enterprise teams"), and Use case ("how do I sync stock across Shopify and Amazon"). Branded prompts stay out.
2. Choose engines. Myrah queries four: OpenAI, Google Gemini, Anthropic Claude, and Perplexity AI, each on fast general-availability tiers, with model versions published so runs can be repeated.
3. Execute cleanly. Send each prompt individually with no system prompt, no history, no appended instructions. Your brand name goes into the matching step, never the prompt.
4. Parse responses. Record whether you were mentioned, where you ranked among the brands the engine named, how you were described, and whether you were cited.
5. Score and store. Myrah's four-stage methodology weights Presence at 55%, Prominence at 30%, and Citation at 15% per engine, averages the four engines equally, then maps to a 0–100 scale.

An AI visibility checker that returns one number is not enough. You need the component split, because that split is the diagnosis.

Diagnose the gap: presence, prominence, or citation

This is the part competing guides skip. The fix for "we are never named" has little in common with the fix for "named fifth" or "described but never linked".

Table 1. AI visibility gap diagnostic: match the symptom to the fix.

Myrah turns each symptom into a ranked task, which is what find and fix visibility gaps does. Tool or spreadsheet, the discipline is the same: one gap, one fix, one re-measurement.

How AI search discovery actually works

Assistants answer from two sources: what the model already contains, and what it retrieves live. Retrieval is the half you influence, and it runs through crawlers you control.

Here is the distinction that trips people up. Each vendor runs separate agents for search indexing, model development, and user-triggered fetches. Blocking a training crawler does not remove you from that vendor's search answers; blocking a search crawler does.

Table 2. Engine, crawler, and what each control governs (as of mid-2026; re-check the vendor docs).

Two footnotes. Perplexity AI documents that Perplexity-User performs user-requested fetches and generally ignores robots.txt, so a robots rule will not stop a user who pastes your URL. And Google states that links in AI Overviews and AI Mode come from Google Search, so a page must be indexed and snippet-eligible (Google); its May 2026 Preferred Sources expansion changed presentation, not ranking (Google). For Google-specific tactics, see improving visibility in Google AI Overviews.

Independent indexes follow the same rule: Brave Search notes that if Googlebot cannot crawl a page, its crawler will not either (Brave). Crawlability is upstream of everything.

The crawlability checklist to run today

Work through this in one sitting. It is the cheapest AI searchability win, and usually where a total-absence Presence gap starts.

• Read robots.txt line by line. Look for wildcard Disallow rules and named blocks on OAI-SearchBot, Claude-SearchBot, or PerplexityBot.
• Confirm you are not blocking a search agent while allowing a training agent. That combination is common and backwards.
• Set training policy separately. GPTBot, ClaudeBot, and Google-Extended are model-development controls.
• Check for noindex on pages you want quoted, including paginated category pages and docs.
• Verify key pages are indexed and snippet-eligible, since Google's AI features draw links from Search.
• Check rendering. If your core answer only exists after client-side JavaScript, assume some fetchers miss it.
• Keep an XML sitemap current. Microsoft documents sitemaps, Bingbot crawlability, and IndexNow for freshness in Copilot Studio's public-web grounding, which uses Bing Custom Search (Microsoft).
• Check WAF and rate limits. Vendors publish IP ranges so you can verify real crawlers rather than block them by accident.

On-page work: six factors an audit inspects

Myrah's audit inspects six on-page factors. Vagueness on the last is the most common reason a brand is described inaccurately.

Table 3. The six on-page factors and what good looks like.

Before and after: a weak paragraph turned into a liftable answer

Take an invented example: Stocklane, an inventory tool for multichannel sellers.

Before: "Stocklane has been designed from the ground up to help modern commerce teams take control of their operations and make managing the business easier than ever."

Nothing there can be lifted. It names no category, no buyer, no capability, no boundary.

After: "Stocklane is multichannel inventory software for ecommerce sellers who list the same stock on more than one storefront. It syncs stock counts across Shopify, Amazon, and eBay so a sale on one channel updates the others automatically. It is built for teams managing 500 to 50,000 SKUs; it does not handle warehouse robotics or 3PL billing."

That version answers the category question, names the buyer, states the mechanism, and marks a boundary. A model can quote any sentence without the rest. Apply the pattern to your homepage, top category pages, and every comparison page you own.

Off-site: the sources you do not own

Assistants synthesize from third-party text as much as from your own site. Comparison articles, "best tools for X" listicles, review platforms, and community threads all feed the answer. When a model names five vendors and you are not one, the source is often a listicle you have never read.

Three things move this, in order of reliability:

1. Be present where category roundups get written. Analyst directories, review platforms, and integration marketplaces are structured, recrawled often, and heavily quoted.
2. Give writers a citable primary source. Publish pricing, integrations, and specs as plain text, so whoever writes the comparison can copy something that credits you.
3. Show up in community discussion honestly. Threads where practitioners describe real usage carry weight and are hard to fake.

Be honest with stakeholders. This work is slow, largely outside your control, and it is where an AI visibility programme stalls. On-page changes can show in a re-run within weeks; third-party corpus change is a two-quarter project.

What does not work

This section will save you money. Google LLC states directly that llms.txt files, artificial content chunking, AI-only rewrites, and special AI schema are not requirements for its AI features, and that the durable guidance is crawlability, technical clarity, and unique useful content (Google). Vendors selling llms.txt as the fix are selling a file Google has said it does not require.

Nothing here guarantees placement. Eligibility is not selection. Any tool or agency promising a citation in ChatGPT or a slot in AI Overviews is describing an outcome nobody controls.

One more distinction: the Gemini API's google_search grounding returns inline citation annotations for developers (Google), a different mechanism from AI-feature eligibility in Google Search. Do not read developer grounding behaviour as evidence about consumer AI Overviews.

A prioritized 30/60/90 plan

Sequence matters: content work is wasted on a page no search agent can fetch.

Table 4. A 90-day AI visibility plan, ordered by dependency.

Re-measure on a schedule or read noise

Model output varies between runs even with identical inputs. Run your prompts once before and once after a change and you are as likely to be reading variance as improvement.

Three rules keep the loop honest. Freeze the prompt set; if you add prompts, treat it as a new baseline. Fix the cadence, monthly or biweekly, rather than re-running whenever someone asks. And read components separately, because Presence at 55% can rise while Citation at 15% falls and the average hides it.

Myrah's flow ends in the same place: run the audit, read the pattern, draft the fix, track the movement. Variability is reported rather than smoothed away, the only way to tell a real gain from a good day.

Frequently asked questions

How do I get my brand mentioned in ChatGPT?

Make sure OAI-SearchBot can crawl your site, since OpenAI documents it as the agent controlling ChatGPT Search eligibility. Then publish pages answering your category questions directly, and earn third-party mentions in comparison content. No method guarantees a mention; these steps make you eligible.

Does traditional SEO still help AI visibility?

Yes, substantially. Google states that links in AI Overviews and AI Mode come from Google Search, so a page must be indexed and snippet-eligible. Crawlability and unique useful content carry over directly. What changes is measurement: rankings say little about whether a model names you.

How long does it take to improve AI visibility?

On-page and crawlability fixes can show in a re-run within a few weeks of re-indexing. Off-site work, where third-party sources must be written and recrawled, usually takes two quarters or more. Set expectations by gap type: Presence gaps from blocked crawlers resolve fastest.

Do I need an llms.txt file?

No. Google states plainly that llms.txt is not a requirement for its AI features, along with artificial chunking, AI-only rewrites, and special AI schema. Spend the effort on crawlability and on answers that stand alone. A vendor selling llms.txt as the fix is telling you about the vendor.

Why does my brand appear in one model but not another?

Because each engine has its own index, crawler, and retrieval behaviour. A robots.txt rule naming one search agent removes you from that engine alone. This is why Myrah averages four engines equally rather than reporting one blended figure that hides an engine-specific block.

What is the difference between AI visibility and AI SEO?

AI visibility is the measurement: how often assistants name, rank, describe, and cite you. AI SEO is the work that changes it. Confusing the two is why teams ship tactics without a baseline. The category boundary is covered in the guide to what AI visibility is.

Conclusion

If ChatGPT does not recommend you, the useful question is not "what tactics should we try" but "which of three gaps do we have". Baseline with unbranded prompts, read Presence, Prominence, and Citation separately, apply the matching fix, then re-run on a fixed cadence. That loop is how to improve brand visibility in AI search engines without guessing.

Your next step is the baseline. Run an AI visibility audit against your real competitor set and find which component is holding you back.

Sources

• Google LLC, AI features and your website
• Google LLC, AI optimization guide
• Google LLC, Google-Extended
• Google LLC, Original, high-quality content in Search
• Google LLC, Grounding with Google Search
• OpenAI, Bots and crawlers
• Anthropic, Does Anthropic crawl the web?
• Perplexity AI, Perplexity crawlers
• Brave, Brave Search crawler
• Microsoft, Generative AI with public websites
• Schema.org, FAQPage