Generative Engine Optimization (GEO)
Kornspire engineers visibility in AI assistants and generative search: entity clarity, consistent factual grounding, citable content structure, machine-readable company data and AI-mention monitoring.
The problem
When an AI assistant describes you, it gets you wrong
Ask an assistant what your company does and you may get a plausible answer assembled from a stale directory listing, a competitor comparison and an inference. Generative systems reconcile what they find; where your own site is vague, inconsistent or unreachable, they fill the gap from elsewhere. The result is a description of your business written by your least accurate source.
Signals you have this problem
- AI assistants describe your services inaccurately or omit them entirely
- Your company name, description and details differ across your own pages and third-party profiles
- Competitors are named in AI answers about your category and you are not
- Nobody at your organisation has ever checked what assistants say about you
Our approach
One consistent entity, grounded facts, citable structure
We establish a single canonical description of the organisation — what it is, what it does, who it serves, where it operates — and make it consistent everywhere, including machine-readable Organization data. Then we structure content so passages can be lifted and attributed: specific claims, defined terminology, self-contained sections, dated updates. Finally we measure what assistants actually say, because GEO without observation is guesswork.
Entity definition and consistency
One canonical company description, service taxonomy and terminology, applied consistently across the site and third-party profiles.
Machine-readable organisation data
Organization, WebSite and Service structured data, an about page that states facts plainly, and same-as links to authoritative profiles.
Citable content structure
Self-contained sections, specific rather than promotional claims, defined terms, visible dates and clear authorship.
Topical depth
Genuine coverage of the subjects you want to be associated with — depth is what makes a source worth citing, not keyword density.
AI crawler access
Deliberate decisions about which AI crawlers may access what, implemented in robots directives, plus an llms.txt index where it adds value.
AI visibility monitoring
Periodic prompting of major assistants with a fixed question set, recording whether you are mentioned, how accurately, and what is cited.
Correction of inaccurate facts
Tracing where a wrong description originates and correcting it at the source your own site or a third-party profile controls.
Outcomes
What changes for you
- One consistent description of the organisation everywhere it appears
- Machine-readable company facts that match the visible page
- Content structured so a passage can be quoted and attributed
- A recorded baseline of what assistants currently say about you
Deliverables
What you receive
- Canonical entity description and terminology guide
- Organization, WebSite and Service structured data implementation
- Third-party profile consistency audit
- AI crawler access policy and llms.txt where warranted
- Baseline AI visibility report against a fixed prompt set
- Content structure recommendations for citability
Evidence
What you can check before you call
Each item below links to something you can verify yourself — our own product, this website, or the engineering playbook we publish.
- This website
Structured data built from page content
Every page carries schema.org markup generated from the same content it renders, so markup and page cannot disagree.
Check a page in Google's Rich Results Test(opens in a new tab) - This website
A generated llms.txt
A machine-readable company summary generated from the site's own content, so it cannot drift from the pages.
Read /llms.txt
How it runs
The engagement, step by step
- 01
Baseline
Ask the major assistants a fixed set of questions about your company and category, and record verbatim what they say today.
- 02
Define the entity
Agree one canonical description, service taxonomy and terminology, then reconcile every place they appear.
- 03
Implement
Structured data, about-page facts, same-as links, citable content structure and crawler access decisions.
- 04
Re-measure
Repeat the prompt set on a schedule and report the change in mentions and accuracy, with honest attribution of cause.
Typical engagements
What this looks like in practice
Illustrative shapes of work rather than client stories. We publish client work only with the client's consent and with real figures, so these describe the problem and the approach instead.
Correcting how assistants describe a company
Finding the stale sources behind a wrong description and replacing them with consistent, machine-readable facts you control.
Establishing presence in category answers
Building genuine topical depth and citable structure for the subjects you want to be associated with.
Deciding an AI crawler policy
A deliberate position on which AI crawlers may access which content, implemented and documented rather than left to default.
Answers
Generative Engine Optimization (GEO): questions
What is Generative Engine Optimization?
Generative Engine Optimization is the practice of making an organisation and its content clear, consistent and citable to AI systems that generate answers — such as ChatGPT, Google's AI features, Perplexity and Copilot — so that when those systems describe your category, they describe you accurately.
Can you guarantee our brand will appear in ChatGPT answers?
No. No supplier can guarantee placement in a generative answer: the systems are non-deterministic, they change without notice, and none of them sells ranking. What can be done is make your organisation unambiguous, factually consistent and genuinely citable, and then measure what assistants say over time. Any guarantee of AI rankings should be treated as a warning sign.
Is GEO just SEO with a new label?
It shares a foundation and diverges in emphasis. Both need crawlable, well-structured content. SEO optimises a page's standing in a ranked list; GEO optimises whether a system that is writing an answer treats your content as a reliable, attributable source about a clearly identified organisation. Entity consistency, factual specificity and citability matter more in GEO than any conventional ranking factor.
Does llms.txt actually do anything?
It is a proposed convention, not a standard that major AI systems commit to honouring, and it is not a substitute for ordinary crawlable content and structured data. We will add one because it is cheap and can help agents orient themselves, while being clear that it is not the mechanism doing the work. Anyone presenting llms.txt as an AI-visibility solution is overstating it.
Should we block AI crawlers?
It is a genuine trade-off and it belongs to you. Blocking protects content from being used for training and summarisation; it also reduces the chance of being cited in answers that reach potential customers. A common middle position is to permit access to public marketing and educational content while restricting proprietary or gated material. We implement whichever position you choose and document it.
How is GEO performance measured?
By repeating a fixed set of prompts across the major assistants on a schedule and recording whether you are mentioned, whether the description is accurate, and which sources are cited, alongside Bing Webmaster Tools' Copilot citation report where it applies. It is mostly sampling rather than analytics, and we present it as such — with the prompt set published so the numbers can be checked.
Deep dives
5 detailed articles on this topic
Longer technical pieces, each covering one specific decision inside this service. Written to be argued with, not skimmed.
Keep reading
Related services
Considering generative engine optimization (geo)?
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