GEO & AI discoverability

GEO & AI Discoverability for Corporate Websites

Answer engines summarise before people click. Whether your organisation is described accurately in that summary depends on how legible your published information is — not on any technique that can promise placement. This is signal work: make the entity clear, the facts consistent and the content machine-readable, then measure how the description changes over time.

What GEO actually is

Generative engine optimisation is the practice of making an organisation's public information consistent, structured and easy to extract, so systems that generate answers have accurate material to work from. It overlaps with SEO but the target is different: SEO competes for a position, GEO competes for an accurate description.

No responsible practitioner can promise inclusion in an AI answer. Ranking and citation behaviour is opaque, changes frequently and differs per engine. What can be improved is the input: clarity, structure, consistency and accessibility of your own published facts.

Entity clarity: one set of facts, everywhere

Most corporate sites contradict themselves. Founding year appears three ways, project counts differ between the about page and the footer, office locations are incomplete on one template and expanded on another. Every inconsistency weakens the entity and gives generated summaries room to guess.

The fix is structural: define a single source of truth for organisational facts, render every surface from it, and keep the same wording across the site, structured data, the LLM-oriented text files and external profiles.

One canonical description of what the organisation does, reused verbatim across surfaces.
Consistent figures with a documented definition for each claim.
Complete location, contact and language information on every relevant template.
Named services with their own pages, rather than a single undifferentiated list.

Technical signals that make content extractable

Answer engines can only use what they can fetch and parse. That makes the technical layer a prerequisite, not an optimisation.

Structured data: Organization, Service, Article, FAQPage and BreadcrumbList where genuinely applicable.
Server-rendered or prerendered HTML so key content exists without executing JavaScript.
robots.txt that explicitly permits the crawlers you want, and a clean, current sitemap.xml.
llms.txt as a concise, human-written summary of the organisation and its main pages.
Canonical URLs, stable slugs and redirects for every retired address.
Semantic headings and real text — not facts locked inside images or decorative components.
Accessibility and performance work, which improves extraction as a side effect.

Content that answers questions directly

Generated answers favour content that states things plainly. Service pages that define scope, process and boundaries; FAQ entries written as real questions with complete answers; insight articles that explain criteria instead of asserting superiority.

Tone matters for the same reason. Unverifiable claims — "the best", "guaranteed results", "number one" — are the parts most likely to be dropped or contradicted. Evidence-based language with dated, checkable facts survives summarisation far better.

External profiles and consistency of record

An entity is corroborated by sources beyond its own site: directory and review profiles, professional networks, award records, press coverage. When name, description, location and links match across those sources and are declared in structured data, the picture becomes harder to misread.

We treat this as maintenance rather than a one-off task: profiles are reviewed periodically and updated from the same source of truth as the site.

Measuring signal work honestly

The measurable part is the input and the description. Track structured data validity, crawl and index coverage, prerendered content parity, consistency of facts across surfaces, and how answer engines describe the organisation when asked over time.

We do not report guaranteed AI citations, and we would be sceptical of anyone who does. The commitment is to signal quality and periodic observation, not to a specific outcome inside a system we do not control.

Frequently asked questions

Can you guarantee our brand appears in AI answers?: No. Answer engines choose their own sources and change behaviour frequently. What we can do is improve the clarity, structure and consistency of the information they read, and observe how the description changes over time.
Is GEO different from SEO?: They overlap heavily in technical foundations. The difference is emphasis: SEO optimises for placement in a results list, GEO optimises for an accurate, extractable description of the entity and its services.
What is llms.txt and do we need it?: It is a plain-text summary of the organisation and its key pages, placed at the site root for language models. It is not an official standard and it will not force inclusion, but it is inexpensive and gives a clean canonical description.
Does this work on an existing website?: Usually yes. Most of it is technical and editorial: structured data, prerendering, sitemap and robots hygiene, fact consistency and page-level clarity. A rebuild is only needed when the current platform cannot render crawlable content.

Want to know how AI describes your organisation?

Share your domain and the questions your audience asks. We will review your current signals — structure, consistency and crawlability — and outline what we would improve first.