Engineer the signals LLMs trust
Large language models do not pick sources at random. They reward entities they recognise, content they can extract, and brands corroborated across the open web. We build the technical foundation that turns your brand into a default citation across GPT, Claude, Gemini, and Llama.
What we deliver
LLM retrieval audits
We test how each major LLM (GPT, Claude, Gemini) currently retrieves and represents your brand, recording entity recognition accuracy, citation frequency, source attribution, and competitor share of voice. The audit identifies whether your brand is missing from the model's knowledge entirely, recognised but unfavoured, or recognised but inaccurately described. Each scenario requires a different fix, and the audit produces a prioritised remediation list with effort and impact estimates so you can sequence the work for maximum return.
Entity and knowledge graph building
We establish your brand as a recognised entity across the structured data layer of the web: Wikipedia, Wikidata, Google Knowledge Graph, Crunchbase, and the major industry directories LLMs use for corroboration. This includes notability assessment, draft preparation, neutral-tone editing, and ongoing maintenance. Entity recognition is one of the strongest signals in LLM citation decisions, brands with knowledge graph presence get cited two to three times more often than equivalent brands without one.
Content structure engineering
We restructure priority pages so LLMs can extract clean, attributable answers. Each page leads with a 40 to 60 word definition, uses scannable subheadings tied to the questions buyers ask, and includes FAQ blocks, comparison tables, and direct-answer paragraphs throughout. Long-form depth stays intact below the extractable layer. This engineering work typically lifts citation rates within four to six weeks because the underlying authority is already there, the content just needed to be readable by machines.
Authoritative citation building
LLMs corroborate brand information across multiple trusted sources before citing it. We run a digital PR programme that targets the specific publications and directories LLMs treat as authoritative: industry trades, major news outlets, association directories, and high-trust review sites. Every placement is selected for its influence on retrieval ranking, not for the backlink alone. The compounding effect is significant, three placements in the right sources outweighs thirty placements in the wrong ones.
NAP and entity consistency
We audit and standardise your Name, Address, Phone, and core entity attributes across every directory, social profile, schema block, and citation source on the open web. Inconsistency confuses LLMs and actively suppresses citations. Standardisation removes that friction. We also implement Organization, LocalBusiness, and Person schema with sameAs links pointing to every authoritative profile, giving the models a single source of truth they can resolve against.
Citation share measurement
We track LLM citation share across GPT, Claude, Gemini, and Perplexity weekly, benchmarking against your top three competitors. Reports include query-level wins, sentiment analysis, source attribution, and the downstream traffic and conversion impact of each citation. You'll see exactly which entity signals are paying off, which content restructures are lifting citation rates, and where the next round of investment should land. No vanity metrics, only the numbers that move revenue.
How we work
Retrieval audit
We benchmark how each major LLM currently sees your brand, test entity recognition, record citation frequency across 50+ priority queries, and identify the structural gaps suppressing your visibility. The output is a remediation list ordered by impact, so the first round of work attacks the highest-impact fixes.
Entity foundation
We build the entity layer: Wikipedia and Wikidata presence where eligible, NAP standardisation across all major directories, Organization schema with full sameAs linking, and knowledge graph consolidation. This is the foundation every other tactic depends on, so it goes first.
Content engineering
We restructure priority pages for extractability, add FAQ and HowTo schema, tighten definition paragraphs, and implement the structural patterns LLMs reward. Every change preserves existing SEO equity while making pages significantly more quotable. Real-time citation wins typically follow within four to six weeks.
Authority and iteration
We run a targeted digital PR programme to earn citations from the sources LLMs trust most, then track citation share weekly and iterate on the changes that move the metric. LLM ranking signals shift constantly, so ongoing measurement and adjustment is the difference between a one-off lift and a defensible long-term position.
Related GEO services
Other disciplines in our AI search programme. Together they cover every surface where buyers ask AI for a recommendation.
Frequently asked questions
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