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ChatGPT & Perplexity Visibility
Become the brand AI assistants name when buyers ask.
Overview
What we do & why it matters
When a buyer asks ChatGPT or Perplexity “best tool for X”, your brand should be the citation. We build the entity authority and citation-worthy content that make these assistants recommend you — and route the resulting demand into your CRM.
What’s included
- Prompt-set research & tracking
- Citation-worthy content
- Entity & digital-PR authority
- Competitor share-of-voice analysis
- Per-engine monitoring dashboard
- AI-lead CRM integration
Part of our GEO engagement · from transparent pricing.
Diagnosis first
Find out whether you are even readable.
Before strategy, check access. Take a distinctive sentence from a key page, ask an assistant with browsing enabled to find pages containing it, and see whether your URL comes back. If it does not, no amount of content work will help until that is fixed.
Crawler access
Different assistants rely on different indexes and user agents. A robots rule or a firewall rule can make you invisible to one while you rank normally in another.
Rendering
Content that appears only after scripts run may never be seen. What matters is the page as delivered, not as displayed.
Source competition
Assistants lean on sources with clear structure and repeated corroboration — often review platforms and communities rather than vendor sites.
Wrong facts about you
When a model describes your company incorrectly, the fix is upstream: the outdated pages and profiles it learned from, not the prompt.
Run the same test on two competitors. If they are found and you are not, the problem is your site. If nobody is found, the topic is being answered from other source types entirely.
Then the build
Turning access into presence.
Mechanics
The two assistants retrieve differently, so the work differs
They get grouped together because both cite sources visibly, but what they do before citing is not the same. Optimising for one as though it were the other wastes effort and produces results in whichever happened to match.
Perplexity searches first
It runs queries, fetches results and summarises them with visible citations. That makes classical findability the main lever and the results legible to measure.
ChatGPT decides whether to search
It answers from memory unless the question signals a need for current information. For many brand questions it never retrieves at all.
Different source preferences
Perplexity leans toward pages that read as reference material; ChatGPT favours sources it can quote cleanly without disclaimers.
Crawler access
Each uses its own user agents. A robots rule written years ago for a different purpose can be the entire reason you are absent.
Rendering tolerance
Content that only appears after client-side JavaScript is unreliable here. If it matters, it belongs in the initial response.
Follow-up behaviour
Both suggest next questions, and those chains are where buyers narrow a shortlist. Winning the first answer matters less than being present across the chain.
We check your access, rendering and current presence on each separately, and report them separately, because a blended score hides which one is actually broken.
Off-site
What gets cited about you is mostly not on your site
On brand, comparison and credibility questions, both assistants prefer sources they read as independent. Your own pages compete for a minority of the citations, which means part of this programme happens on properties you do not own.
Listing and directory hygiene
Stale descriptions quietly become the basis for wrong answers. Among the cheapest fixes available and the most frequently skipped.
Review platforms
Heavily drawn on for ‘best’ and ‘alternatives’ questions, because their structure reads as comparative evidence.
Community discussion
Where buyers ask each other. Showing up honestly under your own name works; manufactured praise is detectable and we will not do it.
Earned coverage
Trade press and analyst mentions carry weight on credibility questions precisely because you did not write them.
One consistent story
The same description and the same facts everywhere. Contradictions across sources make an assistant hedge or choose wrongly.
Tracing wrong answers
An incorrect claim almost always has a single identifiable source. Finding it is the work; correcting it is usually a five-minute edit.
Monitoring
How we track something with no console
Neither assistant provides reporting, so measurement is constructed and run on a schedule. What that actually means:
Movement under roughly five points between cycles is inside normal variance. We say so rather than presenting noise as progress.
Content
What these two actually choose to quote
Across the prompt sets we run, the pages that get cited have less in common with conventional marketing pages than with reference material. The pattern is consistent enough to design against, even though neither platform documents it.
A stated answer, early
The claim made plainly in the opening lines, in the question's vocabulary. Pages that build to a conclusion lose to pages that lead with one.
Passages that stand alone
Extraction happens at section level. A paragraph that only makes sense after the three above it will be skipped or misquoted.
Numbers and named conditions
Specifics survive summarisation; adjectives do not. ‘Typically two to three weeks, depending on sales cycle’ gets quoted. ‘Fast turnaround’ does not.
Explicit scope
When something applies and when it does not. Qualified statements are safer for an assistant to repeat, so they get repeated.
Comparison handled directly
Pages that address alternatives honestly are reached for on exactly the questions where purchase decisions get made.
Clean, server-rendered markup
Real headings and lists, present in the initial response. Content assembled client-side is unreliable here, and sometimes invisible.
None of this means writing for machines. It means putting the answer where a hurried human would also want it, which is the same change.
Honest limits
What cannot be promised
This is the part of the market with the most confident claims and the least ability to deliver them. Here is where the real boundaries sit.
Guaranteed citations
Answers are generated probabilistically and vary run to run on the same prompt. Anyone guaranteeing placement is selling something they cannot control.
Fast changes to model memory
Where ChatGPT answers without retrieving, you are waiting on training cycles nobody outside the lab can schedule. We flag which questions fall in that bucket.
Paying for placement
There is no media buy here. Anyone offering guaranteed inclusion is describing either a misunderstanding or a scam.
Control of phrasing
You can improve the evidence and correct the record. You cannot dictate wording, and you should be wary of anyone implying otherwise.
Winning on an empty page
If there is nothing substantive to cite, no amount of structure helps. Assistants are not short of generic sources.
Stability
Both products change often and sometimes abruptly. What survives redesigns is accurate, well-structured, genuinely useful content, so that is what the work builds toward.
Questions
ChatGPT & Perplexity Visibility, answered.
Can you guarantee citations?
No one can guarantee an AI’s output, but we systematically improve the signals that drive citations and track measurable gains in share-of-voice.
How fast do citations appear?
Some shift within weeks as content and authority land; compounding gains build over the first two quarters.
Why do we appear in Perplexity but not ChatGPT?
Almost always because ChatGPT did not search. It answers from model memory unless the question signals a need for current information, and brand questions frequently do not trigger retrieval. If you are absent from memory, the fix is slower and mostly off-site — third-party coverage and consistent descriptions across the web — rather than anything you publish on your own domain. We identify which of your target questions trigger retrieval and which do not, because the two need different work.
Are we blocking their crawlers by accident?
It happens more often than you would expect, usually from a robots.txt rule added years ago for a different reason, or a bot-protection product with aggressive defaults that nobody revisited. Each assistant uses its own user agents, so you can be visible to one and invisible to another. This is the first thing we check, it takes minutes, and it is occasionally the entire explanation for an absence.
Does it help to publish an llms.txt file?
It is cheap and harmless, so we add one where it fits. We would not want you to expect much from it: adoption is inconsistent, Google's search systems do not use it, and no amount of a manifest file compensates for content nothing wants to cite. Treat it as hygiene alongside a clean sitemap, not as a lever.
How many prompts should we track?
Enough to be representative and few enough to re-run honestly every cycle — in practice between thirty and a couple of hundred. The set is built from how your buyers describe the problem rather than from keyword exports, it stays fixed so comparisons hold, and it gets reviewed quarterly as your market's language shifts.
Can you remove something wrong that an assistant says about us?
Usually, if it is coming from retrieval. Wrong claims nearly always trace to a specific source — a stale listing, an old pricing page, an outdated article, inconsistent descriptions across your properties. Correct the source and retrieval-based answers follow within weeks. Claims baked into model memory are harder: you can dilute them with consistent, current information everywhere else, but the timeline runs on training cycles rather than on your schedule.
Is this worth doing if our category is small?
Often more worth it than for a large category, because there is less competition for the citation and assistants are more likely to reach for whichever source actually answers the question. The honest caveat is volume: if few people ask about your category at all, winning every answer still produces a modest number of visits. We would rather tell you the ceiling before you commit than report a strong citation share against a tiny denominator.
How does this relate to the SEO programme?
Perplexity runs searches before answering, so findability in conventional search feeds it directly, and improvements there show up here. ChatGPT is looser about ranking but still rewards pages that are accessible, fast and clearly structured. The two programmes share foundations and we run them as one, with separate reporting so you can see which surface moved.
Should we create content specifically for assistants, separate from our normal pages?
No, and we would argue against it. A page built only to be quoted tends to read as thin to humans, and both platforms are reasonably good at discounting that. The changes that win citations — leading with the answer, attaching evidence to claims, writing sections that stand alone, stating where something does not apply — also make the page better for the person reading it. If a proposed change helps the assistant and hurts the reader, it is the wrong change.
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