What the AI Visibility Audit checks

An audit is a structured assessment of how well your brand is set up to earn AI citations — built from your own scan data, not a generic checklist. It reads your citation history, your site, and your competitive position, then scores six categories and produces a prioritised list of what to fix first.

​

What an audit actually is

Your AIS Score tells you how visible your brand is in AI answers right now. An audit answers a different question: why, and what to do about it. It’s grounded in your last 90 days of Surfais scan data — your citation history by platform, your branded vs non-branded query mix, your top competitor mentions, your alias coverage — combined with a fresh technical read of your site (crawler access, schema, rendering) and your visibility elsewhere online (Wikipedia, review sites, forums, Google Search Console where connected). That combination is scored against six weighted categories, producing a single Readiness Score out of 100. Every recommendation underneath it is checked against your brand’s category — industry, geography, and scale — so what you’re told to do is something you can actually do. See AIS Score vs Readiness Score for how the two numbers relate and why they’re never merged.

​

The six categories

Each category is scored 0–100% of its available weight, then combined into the Readiness Score. Citability and Authority carry the most weight by design — earned, citable content is the dominant channel AI platforms actually draw answers from, and it’s the one thing every brand can influence directly regardless of size or budget.

​

Citability

Does your content give an AI platform something clean to lift and cite? This category checks for self-contained answer blocks — roughly one clear answer per section, not a conclusion buried three paragraphs down — and answer-first structure, where a page opens with a direct definition (“X is…”) rather than working up to the point. It also rewards evidence-bearing content: quotations from credible sources, unique statistics with attribution, cited sources. Keyword stuffing and vague, unsupported claims score poorly here — padding content doesn’t help you get cited, it just makes the real answer harder to find.

​

Authority & Brand Signals

This is where earned, independent recognition of your brand lives: press coverage, author bylines with real credentials, and entity presence — a Wikidata entry, a Google Knowledge Panel, a well-maintained LinkedIn company page, a Wikipedia article where one is actually attainable. AI platforms lean heavily on third-party validation over anything you say about yourself, so this category rewards proof other people vouch for you, not marketing copy.

​

Structural Readability

Even strong content can be hard for an AI platform to parse if it’s poorly organised. This category checks your heading hierarchy (a real H1 → H2 → H3 structure, not styled paragraphs pretending to be headings), question-based subheadings that mirror how people actually ask AI platforms things, visible comparison content in lists or tables rather than dense prose, and clean semantic HTML underneath it all.

​

Technical AI-Readiness

Content that’s excellent but unreachable doesn’t get cited. This category checks whether the crawlers AI platforms use to discover and retrieve your content are actually allowed in — and, separately, whether your key content renders in the initial page load rather than needing JavaScript to appear, which matters because several major AI platforms fetch raw HTML without executing scripts. It also checks your structured data (schema markup): whether it’s present, accurate, and matches what’s actually visible on the page, which is the one universal requirement every platform agrees on.

​

Branded-share Health

This looks at the balance between people asking about your brand by name and people asking category questions where you could be recommended but aren’t named yet. A healthy mix — and a mix that’s trending the right direction over your last 30 days — scores well. A brand that’s only ever found by name has a discovery problem; a brand that’s active in category conversations but never gets named for its own brand has a different one. Alias coverage (the variant names and spellings Surfais tracks for you) feeds into this too.

​

Multi-modal Coverage

The lightest-weighted category, deliberately — it checks whether your content spans text, image, and video with proper descriptive metadata, giving AI platforms additional surfaces to draw from. It’s a genuine plus, but the evidence behind it is thinner than the other five, which is exactly why it carries the smallest share of the total.

​

Recommendations are filtered to what you can actually do

This is the part that separates a Surfais audit from a generic checklist: every recommendation is checked against your brand’s industry, geography, and scale before it’s allowed to reach you. A recommendation that doesn’t clear that filter is either dropped or swapped for something that actually applies — and when a substitution happens, the audit tells you why. A few concrete examples of how this plays out:

• Wikipedia. The default advice everywhere is “get a Wikipedia page.” Surfais only recommends this when your brand has a realistic path to notability — independent press coverage from multiple named publications, roughly. A small local business without that coverage isn’t told to chase a Wikipedia article it can’t get (and would never be advised to write one about itself either way); instead the audit points to a Wikidata entry, a Google Knowledge Panel, or a Crunchbase profile — lower-bar entity signals that feed the same underlying visibility.
• Reddit engagement. Useful for plenty of brands, but not universally. An Irish local-service business, for example, is steered toward Boards.ie and trade-press guest posts instead — the forums where that conversation actually happens — rather than generic Reddit advice that doesn’t fit the market.
• Review platforms. The audit routes you to the review platform your specific engine mix and industry actually draws from — B2B software gets pointed at different platforms than local services or e-commerce — rather than a one-size-fits-all “collect reviews” line.

The same logic applies across LinkedIn thought-leadership, original research, AI-crawler access, and schema markup — each has its own set of conditions in the underlying framework for when it’s recommended, substituted, or skipped. Nothing you’re told to do is generic advice with your brand name swapped in.

​

What an audit is not

An audit is not your AIS Score, and the number it produces — the Readiness Score — is never called “AIS” anywhere in Surfais. They measure different things: the AIS Score is your live, measured visibility across AI answers; the Readiness Score is this audit’s assessment of how well-prepared you are to earn more of it. A brand can have a strong AIS Score sitting on shaky groundwork, or a modest AIS Score despite having a genuinely well-built site — the two are designed to diverge, and a gap between them is information, not a bug. Read AIS Score vs Readiness Score for the full picture of how to read that gap. An audit also isn’t real-time — it’s a snapshot, re-run periodically, that reflects what’s changed since the last one. Between audits, your AIS Score is what moves.

​

Related pages