AI Retrieval GEO Strategy llms.txt Structured Data

Generative Engine Optimisation (GEO) – Native Content Systems

Optimise your content to be cited, surfaced, and accurately represented by AI assistants — Perplexity, ChatGPT Search, Google AI Overviews, and Claude. We build the technical foundation that makes your brand AI-retrieval ready.

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Search is no longer a list of blue links. When someone asks an AI assistant about your industry, your competitors, or the problem your business solves, the answer comes from a system that retrieves, summarises, and cites — without the user ever clicking a URL. If your content is not structured for that retrieval layer, you are invisible to it.

Generative Engine Optimisation (GEO) is the technical discipline of making your content accurately understood, retrieved, and cited by AI-powered search systems. It is distinct from traditional SEO — though the two compound each other — and it requires its own architectural approach.

Why GEO Is Different from SEO

SEO optimises for a ranking algorithm. GEO optimises for a language model’s retrieval and summarisation pipeline. The signals are different, the technical requirements are different, and the content structure that earns citation in an AI-generated response is different from the content that earns a top-10 ranking.

Both matter. But they are not the same work, and conflating them produces results that underperform on both dimensions.

What GEO-Native Systems Include

llms.txt Implementation: A structured, plain-text site manifest at the root of your domain — the emerging standard for communicating your organisation, services, and key pages directly to AI crawlers in a format they parse efficiently. Dynamic, version-controlled, and always in sync with your live content.
Speakable Schema Markup: SpeakableSpecification embedded in your WebPage schema graph, annotating the precise CSS selectors that contain your most citation-worthy content. AI voice interfaces and retrieval systems use this to identify authoritative text regions without full-page parsing.
AI Citation Meta: Author attribution, geographic signals, content classification, and language declarations in <head> — the metadata that AI assistants read to contextualise your content before summarising it.
AI Bot Policy via robots.txt: Explicit directives separating AI search crawlers (Perplexity, GPTBot, ClaudeBot, Google-Extended) from training-data scrapers (CommonCrawl, Diffbot). Opt-in to citation, opt-out of training — as separate, intentional choices.
Entity-Rich Schema Graphs: Organisation, Service, ItemList, and OfferCatalog entities that let AI systems understand your full service offering, founder identity, and topical authority without inference. Structured data that answers the question before it is asked.
Content Architecture for Retrieval: Page structure, heading hierarchy, and content patterns that align with how retrieval-augmented generation systems parse, chunk, and score source material for relevance and authority.

Measurement and Monitoring

We track GEO performance through AI visibility audits — testing how major AI assistants currently represent your organisation, identifying inaccuracies or gaps, and benchmarking citation surface area before and after implementation. This is an emerging discipline, and measurement is evolving alongside the platforms. We work at the frontier.

Whats Included

llms.txt dynamic site manifest creation
Speakable Schema.org implementation
AI citation meta tag architecture
robots.txt AI bot policy (search vs. training)
OfferCatalog + ItemList entity schema
AI visibility audit & citation gap analysis
Content architecture for AI retrieval systems

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How We Work

AI Visibility Audit

We test how major AI assistants currently represent your brand — identifying inaccuracies, gaps, and citation opportunities. This becomes the baseline against which all GEO work is measured.

GEO Architecture

We design the full technical GEO stack: schema graph extensions, llms.txt structure, meta tag strategy, and content architecture — tailored to your specific industry and competitive landscape.

Implementation

We deploy all GEO signals: llms.txt, speakable schema, AI citation meta, robots.txt AI policy, and entity-rich structured data — integrated into your existing WordPress architecture with zero disruption.

Monitor & Refine

We conduct follow-up AI visibility audits to measure citation accuracy and surface area growth, and refine the implementation as AI retrieval platforms evolve their signals.

Frequently Asked Questions

What is GEO and how is it different from SEO?

SEO optimises content for search ranking algorithms. GEO (Generative Engine Optimisation) optimises content for AI-powered retrieval and citation systems — the layer that powers Perplexity, ChatGPT Search, and Google AI Overviews. They use different signals and require different technical approaches, though they reinforce each other when implemented together.

Will GEO conflict with my existing SEO?

No — GEO and SEO are complementary. The schema markup, content structure, and technical signals we implement for GEO strengthen your SEO entity authority at the same time. We design both layers to compound each other.

How do you know if AI assistants are citing us correctly?

We conduct structured AI visibility audits — systematically querying major AI assistants with brand-relevant questions and analysing the accuracy, frequency, and quality of citations. This is part of both the initial audit and follow-up measurement.

What is llms.txt and does it actually work?

llms.txt is a plain-text file at your domain root — analogous to robots.txt but for AI crawlers — that describes your organisation, services, and site structure in a format LLMs parse efficiently. It was proposed by Jeremy Howard (Answer.AI) in 2024. Adoption is growing among AI crawlers and is already in use by several major platforms.