Your buyers are already in ChatGPT and Perplexity. Most brands haven't caught up yet.
A structured audit of how your brand appears across the major AI answer engines. You'll know your citation share, what's missing from your entity profile, and exactly what to fix first.
A full technical, on-page, and content audit that ends with a prioritised backlog. Not a 200-slide deck your engineers will ignore. Something your team can open on Monday and start working from.
Content built to rank in search and get cited in AI answers. You get a topic cluster plan, entity-level briefs your writers can use, and a measurement setup that tracks LLM mentions alongside organic traffic.
The full organic channel, handled. Strategy, content, technical work, links, and reporting. You get senior-level execution and a clear picture of what's working each month.
Crawl health, rendering, Core Web Vitals, structured data, site migrations. The infrastructure work that most agencies skip because it's hard to explain to a client. I'll fix it and explain exactly what it was costing you.
Earned links and press coverage from sources Google and the AI answer engines trust. No link schemes, no guest post spam. Coverage that builds authority and gets your brand into the citations that matter.
If your team knows SEO is important but can't agree on what to do first, this is where to start. You get a prioritised roadmap, the rationale your CFO will need, and a measurement setup that makes progress visible from month one.
No organic presence on non-branded terms as Google rolled out AI answers. The brief: own the category before Avalara, Thomson Reuters, or Sovos did.
Branded-heavy keyword mix, no Wikipedia entry. Completely invisible in AI answers. The brief was about AI surfaces, not search rankings.
Pull the real prompts your buyers type into LLMs and search. Find out where you appear and where competitors are taking the answer instead.
Technical health, entity coverage, citation-worthiness, content depth. Split into what models can see and what's invisible to them.
A prioritised fix list: briefs, schema, IA changes, PR plays. Written for how your team works, not a theoretical backlog.
A dashboard that tracks LLM citations, prompt-level share-of-voice, and assisted pipeline. Traditional rank and traffic sit alongside it.
This is what those four steps produce. I've run them on Nike as a worked example: the entity map, the gap diagnosis, the fix list, and the dashboard that tracks it. Same deliverables you'd get, on a brand everyone knows.
Buyers don't search for brands. They search for problems. The first step maps where you show up across real buyer-intent prompts, and where competitors are taking the answer instead.
The split is almost always the same: strong on branded queries, missing on the prompts that drive pipeline.
Every prompt lands in one of three clusters: owned, contested, or absent. The matrix on the right is a full run, 47 prompts across the major answer engines.
"best shoe for plantar fasciitis"
Nobody starts with a product name. They start with a problem, a use case, or a comparison. So I map non-branded prompts against live LLM responses at every stage of the buying journey.
The pattern holds across every category: brands own the branded layer, then disappear the moment a buyer describes a need rather than a name.
This map decides what gets built first.
On a large site, a single template issue breaks thousands of pages at once. So every fix is scoped by template, not by page.
The most common finding: missing structured data. Schema is what makes a page machine-readable, and without it LLMs struggle to cite you even when the content is good.
You get a prioritised fix list, not a score.
Before anything changes, I log where you sit across every platform and every stage of the buyer journey. Every number that follows gets measured against this.
The baseline below is what that looks like: 140 prompts across four intent clusters, run in parallel on ChatGPT, Google AI Overviews, and Perplexity. Citations extracted, share-of-voice calculated against named competitors.
Schema is what makes content machine-readable to LLMs. At scale it's a template problem, not a page problem: one JSON-LD spec per template, and the fix rolls out across every page built on it in a single sprint.
The diff on the right shows one product page going from 5 fields to 48. The highest-signal single change is usually the simplest: a named subject-matter expert with verifiable credentials as the page author.
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Hub-and-spoke architecture, built around the entities LLMs cite least.
Each hub is one authoritative page on one topic, written by a named expert with machine-readable author schema. Spoke pages intercept long-tail prompts at every buying stage, and their link equity flows back to the hub. The example below builds hubs around two product technologies.
This structure is what generates the citation numbers in the next card.
Not blog posts. Not product copy. Structured, direct-answer pages with clear entity attribution, specific data, and FAQ schema matched to the exact phrasing LLMs use.
LLMs cite pages that hand them a verifiable claim they can repeat confidently. Vague descriptions don't get cited. The example on the right shows the format: one claim, one source, one number a model can quote.
Example content format. Illustrative of the type commissioned, not proprietary assets.
The chain from proprietary data to LLM citation runs in four steps: original finding, earned editorial coverage, indexed across the web, cited as authoritative third-party validation.
Most SEO teams understand the first and last steps and skip the middle.
This is the middle.
LLMs weight content by author credibility, not brand credibility. A page attributed to a named subject-matter expert with peer-reviewed publications and an ORCID ID carries the authority of a scientist.
That's a different citation category entirely.
The model cites the author's claim, with your domain as the source.
This works with named internal experts, cited industry practitioners, or published research partners. You don't need a research lab.
Every engagement closes with a live monitoring system, not a PDF. The full prompt set from the baseline re-runs every week: citations logged, positions noted, share-of-voice tracked against named competitors.
When something regresses, you know within 48 hours. And you know why: new competitor content, a model update, or your content losing freshness. The dashboard below is the view you'd open on a Monday.
Invisible in AI answers, slipping in search, or both. Thirty minutes is enough to work out where to start.
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