The method, published
How AI visibility is earned.
The Answari methodology is a six-phase sequence for making a business visible in AI answers: baseline measurement, crawler access, entity trust, on-page optimisation, authority building, and continuous reporting. It is published here in full, because the clearest proof of a method is showing it - and this site is built with it.
First principles
What are AI engines actually looking for?
When someone asks ChatGPT, Gemini, Claude or Perplexity for a recommendation, the engine builds its answer from sources it can understand, trust and cite. Two levers sit underneath everything we do:
Lever one
Machine-readability
Structure the site so engines never have to guess what anything is: structured data, clean headings, answer-first copy, real HTML tables. The easier a page is to lift accurately, the more often it is lifted.
Lever two
Entity trust
Make the business one consistent, verifiable entity across the whole web - same name, identity and facts everywhere, corroborated by independent sources - so engines are confident recommending it.
Every tactic in the six phases below serves one or both levers. Anything that serves neither is decoration, and we do not sell decoration.
The six phases
The order matters.
Each phase has a clear “done when”. Each depends on the one before it.
Baseline and measurement - always first
You cannot prove improvement without a starting line. We run a fixed set of your buyers’ real questions across ChatGPT, Perplexity, Gemini and Claude, recording whether you appear, in what position, and whether the facts are accurate. We split branded citations from non-branded ones - because a business cited only when people already search its name is invisible in genuine commercial discovery. We also baseline AI-referral traffic and check whether Google recognises the business as a distinct entity.
a one-page “where you stand in AI today” baseline exists.
Crawler access and technical foundation
If engines cannot reach or read the site, nothing else matters. We test whether AI crawlers - GPTBot, ClaudeBot, PerplexityBot and the rest - are silently blocked at the CDN or firewall edge, a surprisingly common and invisible reason a site is absent from AI answers. Then page speed, clean indexation and fast re-indexing. A site can be “allowed” in robots.txt and still blocked at the edge; we check both, because they are independent controls.
AI crawlers reach the site, it is fast, and the index is clean.
Entity and trust
We make the business one confident, verifiable entity: a full structured-data stack led by Organisation schema with sameAs connections to your profiles and the company register, and strict consistency of name, address, phone and identity across every property - website, Google Business Profile, review platforms, LinkedIn. If the engines do not yet recognise the business as a distinct entity, we fix that first.
the business presents as a single, consistent entity the engines can verify.
On-page optimisation
Every key page is made answer-first and quotable: a direct 40-60 word answer at the top, a block of specific verifiable facts, FAQ sections with FAQPage schema, real HTML tables rather than styled boxes, visible freshness dates, and substantial unique content in plain language. Clean question-and-answer pairs are the easiest thing for an answer engine to lift - so we give it exactly that.
key pages lead with the answer and read as the clearest source on the topic.
Authority and citations - the slow compounder
In competitive queries, authority beats schema. Engines recommend businesses that independent third parties already vouch for: editorial mentions, credible directories, named and credentialled people with real author profiles. This work compounds slowly, which is why it starts early and never involves pay-for-placement - engines are increasingly good at telling earned authority from bought noise.
non-branded citations start appearing and growing.
Measure, report, iterate - continuous
The visibility test is re-run on a fixed cadence, tracking citation share, position and accuracy over time against the phase-one baseline. Monthly reporting covers what was done, what moved, and what it produced: visibility won, citations gained, enquiries attributed. The AI platforms change constantly; the plan adapts with them.
never. This is the ongoing work, and the reason the retainer is genuine.
AI engines don’t recommend websites. They recommend trusted entities.
- the premise behind phases iii and v
A key distinction
What wins answers is not what wins recommendations.
Our testing keeps confirming a split that most AI-visibility advice misses:
Direct answers
“What does treatment X involve?”
Won by machine-readability: structured data and answer-first content make you the easiest accurate source to quote. Schema gets you cited.
Recommendations
“Who is the best provider of X near me?”
Won by entity trust: independent editorial citations and named, credentialled people. Authority gets you recommended.
→ and visibility is won question by question, not business by business. The same practice can top one answer and be absent from the next - which is what the audit uncovers.
How the numbers are made
The test conditions.
Answari’s controlled citation-share figures are produced under a fixed testing protocol. Platform-reported data is shown separately and is not mixed into the controlled score.
- A fixed prompt set - at least ten real buyer questions, agreed in writing before the baseline and kept unchanged between reporting periods.
- A fixed direct-test engine set - ChatGPT, Gemini, Claude and Perplexity, tested through clearly recorded product and search settings.
- Clean testing sessions - prompts are run in new sessions with previous conversation context and personal memory excluded wherever the product permits.
- Recorded conditions - each report identifies the test date, product used, relevant browsing or search setting and the testing location.
- Repeat runs for priority prompts - the five most commercially important prompts are tested three times during each reporting period. Inconsistent results are marked as volatile rather than hidden inside one average.
- Evidence retained - answer text, source references and scoring records are retained with the engagement records so reported figures can be traced back to the underlying run.
Google Search Console and Bing Webmaster Tools data may be used as supplementary evidence where relevant. Those figures are labelled separately because platform reporting and direct prompt testing measure different things.
On the method
Questions worth asking us.
Why does the order of the phases matter?
Because each phase depends on the one before it. There is no point writing answer-first content if AI crawlers are blocked at the edge, and no point building authority for an entity the engines cannot verify. Baseline first, access second, entity third, content fourth, authority fifth, measurement always.
Why does every engagement start with measurement?
Because improvement cannot be proven without a starting line. The baseline records whether you appear in AI answers today, in what position, and whether the facts are accurate - and every monthly report afterwards is judged against it. It is also, candidly, the honest way to sell: you see exactly where you stand before committing to anything ongoing.
Is schema enough to win AI recommendations?
No. Structured data wins direct answers - it makes you easy to cite accurately. Recommendations in competitive queries are driven mostly by authority: independent third-party citations and named, credentialled people. A complete methodology needs both, which is why authority building is its own phase rather than a footnote.
Why is the retainer genuine rather than a subscription for its own sake?
AI platforms change constantly and competitors respond. A citation position is not won once; it is defended. The monthly work re-runs the visibility test, reports movement against baseline, and adapts the plan. That is ongoing work by nature, not by pricing design - and if it ever stopped producing movement, the reporting would show it plainly.
Does this replace SEO?
It sits alongside SEO. The two disciplines share important foundations - technical hygiene, useful content, crawlability and clear information.
GEO then examines what happens inside AI-generated answers: whether the business is named, which sources support the answer, whether the facts are accurate and how prominently the business appears.
Neither replaces the other. Our GEO versus SEO guide explains where their methods overlap and where the measurement differs.
Phase one, applied to you
The method starts with your baseline.
The Visibility Audit is phase one run on your business: your buyers’ questions, the major engines, your citation share against every competitor.
See the servicesLast updated: July 2026