Mention audit
Where your brand is referenced across the web today, in what context, and how it is described. Usually the first time anyone has counted rather than assumed.
Brand mention engineering builds the volume and consistency of references to your company across the web, linked or otherwise. Language models absorb mentions during training regardless of whether a link is attached, which makes an unlinked mention nearly as valuable for generative visibility as a linked one.
Typically ongoing, with a three-month minimum.
A link passes ranking signal between pages. A mention teaches a model that your brand exists, what category it belongs to, and what people say about it. Since models learn from text rather than from link graphs, an unlinked mention in a relevant discussion carries most of the value.
That inverts a priority most agencies still work to. Link building has spent twenty years optimising for authority metrics, which meant chasing links from high-scoring domains regardless of whether anyone reads them. For generative visibility, a mention in a widely-read discussion of your category beats a link from a domain nobody visits.
Consistency turns out to matter as much as volume. If your company is referred to four different ways across the web — the legal entity, the trading name, an old brand, and an abbreviation — each variant accumulates its own thin pile of associations rather than one strong entity. Models then struggle to resolve which is which, and treat you as several weak things.
The relevance requirement is where most mention work goes wrong. A mention only teaches a model something useful if it sits in context that identifies what you do. Being named in a general business roundup teaches almost nothing; being named in a discussion of the specific problem you solve teaches exactly the association you want.
None of this is fast. Mention volume moves over quarters because it depends on other people writing about you, and unlike a technical fix there is no version of this you can simply ship. What it buys is durability — accumulated mentions are extremely difficult for a competitor to undo.
The programme covers an audit of where you are mentioned today, a canonical naming standard applied across your own properties, targeted work to earn mentions in the contexts models learn from, and tracking of both mention volume and how consistently your brand is described.
Where your brand is referenced across the web today, in what context, and how it is described. Usually the first time anyone has counted rather than assumed.
One agreed brand string, applied consistently across your site, profiles, documentation and outreach, so variants stop fragmenting your entity.
Mentions pursued in discussions of the problem you solve, rather than in whatever publication has the highest authority score and the fewest readers.
Your named people quoted in coverage of your category, which produces exactly the association models learn from — a person, a company, a subject.
The reference sources models draw on — company profiles, directories, knowledge panels — corrected and aligned to the canonical name and description.
Volume, context and naming consistency tracked over quarters, since this operates on a slower clock than the rest of the pillar.
The programme runs in quarterly cycles: audit the mentions and naming you have today, fix the inconsistency you already control, then pursue mentions in the contexts that teach models something useful. Naming fixes land immediately; earned mention volume moves across quarters.
Count where you are referenced, in what context, and how you are named. Naming inconsistency is almost always worse than clients expect and is entirely self-inflicted.
→ A mention baseline and a naming variant list
One canonical brand string applied across your own properties, profiles and documentation. Free, immediate, and the largest single unlock for entity resolution.
→ Consistent naming across owned properties
Directories, company profiles and knowledge panels aligned to the canonical name and an accurate category description, because models weight these sources heavily.
→ Corrected third-party reference entries
Expert commentary and coverage in discussions of the problem you solve. Slower than link building and considerably more useful for what models actually learn.
→ Mentions in category-relevant coverage
Mention volume, context quality and naming consistency reported quarterly, because a monthly view of this shows noise rather than trend.
→ Quarterly mention and consistency report
They optimise for different systems. Link building targets a ranking algorithm that reads a link graph. Mention engineering targets models that read text. The two overlap in tactics and diverge sharply in what counts as a win, which is why one agency often does both badly.
| Link building | Mention engineering | |
|---|---|---|
| Optimises for | A ranking algorithm reading a link graph | Models reading text |
| Unlinked mention | Worth little | Worth nearly as much as a linked one |
| Target selection | Domain authority scores | Whether the audience discusses your category |
| Naming consistency | Largely irrelevant | Critical — variants fragment the entity |
| Anchor text | Carefully managed | Irrelevant; surrounding context matters instead |
| Time to effect | Weeks to months | Quarters |
You need this when models confuse your brand with another company, when you are referred to several different ways across your own properties, or when a baseline shows engines knowing your category well while never naming you as a participant in it.
Naming consistency is the fast half and frequently produces the largest single improvement in entity resolution, within weeks. Earned mention volume is the slow half, moving over quarters, and it is what makes generative visibility difficult for a competitor to take back.
Each answer is written to stand alone in 40 to 60 words — the shape an AI Overview or Perplexity citation lifts. Ships with FAQPage schema.
For generative visibility, substantially. Models learn from text rather than from link graphs, so a mention in a relevant discussion teaches the association whether or not a link is attached. For classic ranking, links still matter more — the two disciplines diverge here.
Digital PR usually targets coverage volume and links from high-authority publications. Mention engineering targets contextual relevance and naming consistency, so a mention in a well-read category discussion outranks a link from a high-scoring domain nobody reads.
Because each variant of your name accumulates its own separate pile of associations. A company referred to four ways ends up as four weak entities rather than one strong one, and models then struggle to resolve which references belong together.
Naming fixes on your own properties resolve within weeks and are free. Earned mention volume moves over quarters, because it depends on other people writing about you. We report quarterly, since a monthly view of mention data shows noise.
Paid placements are legitimate when disclosed, and carry less weight precisely because disclosure signals they were bought. The durable version is being genuinely worth mentioning, which is slower and is what the expert commentary programme is for.
Then this is urgent. Models retain the old name for a long time, and every month without consistent reinforcement of the new one extends that. Rebrands are the situation where mention engineering produces its clearest measurable gains.
Thirty minutes with a senior strategist. We pull your live visibility while we talk and tell you plainly whether a unlinked brand mentions is what you need — or whether your problem sits somewhere else.