Generative engine optimisation, also called GEO or AI search visibility, is the practice of structuring content and technical data so that AI answer engines — ChatGPT, Perplexity, Google’s AI Overviews, Copilot — can find a page, read it and use it as a source when composing an answer. It builds on technical SEO rather than replacing it, and the target moves from a position in a list of links towards being the material an answer is assembled from.
For an industrial manufacturer the practical driver is how specifying engineers and procurement leads now open a search: describing a duty point, a material compatibility or a certification requirement in a sentence and asking which products meet it. Whether any system cites a given page is decided by that provider, so the work is to improve the factors within reach and measure what changes.
Traditional SEO competes for a position in a list a person scans and clicks. A generative engine reads a set of candidate sources, extracts what it needs and composes an answer, sometimes citing and sometimes not.
The consequences differ. Factual precision, unambiguous naming and a structure a model can segment count for more; persuasive framing and keyword placement count for less. A page can be drawn on without being clicked, which changes what you measure and how you report it.
Instead of a part number, the query arrives as a specification: a pump for a given flow rate and head handling a specific fluid at temperature, a gasket material compatible with a solvent, a sensor with a particular ingress protection rating and approval, a supplier able to serve three European markets.
Content answering those queries names the values explicitly, in units, in text. A specification held in an image, in a PDF, or behind a filter interface that renders after execution is unavailable to a system reading the page.
Publish specifications as HTML tables with labelled units rather than as images or PDF-only downloads. State the application, the material compatibility, the standards met and the approvals held in plain sentences on the product page. Give each product a stable canonical page rather than a variant of a filter URL.
Mark the facts up so the machine reading and the human reading agree. See schema markup for industrial websites for what is worth implementing.
Models resolve which company, brand and product you mean from what they can read across the web. A group with several legal entities, an acquired brand still trading under its former name, and different product naming across country sites sends ambiguous signals.
Use one naming convention across the estate, state the relationships between group, brands and subsidiaries explicitly, and keep the same facts consistent across the site, distributor listings and trade directories. Contradictions between sources reduce the confidence placed in all of them.
The change helping a generative engine is the change helping a specifying engineer: values stated in units, applications described in the words a buyer uses, standards named specifically, and the datasheet reachable without a form.
That is why the work stands up even where AI referral volume stays small. Our AI search visibility page sets out the programme, and AI search and industrial brands covers the wider shift.
Citation and inclusion decisions belong to each provider’s own system and change without notice. An offer of guaranteed citation, guaranteed inclusion in AI Overviews or a fixed position in an assistant’s answer describes something outside the supplier’s control.
What can be done is to establish the current position, improve the structural factors, and measure again. See AI search visibility audit for how that baseline is taken.
It is the practice of structuring content and technical data so AI answer engines can find, read and use a page as a source when composing an answer. Also called GEO or AI search visibility, it builds on technical SEO rather than replacing it, and the target moves from a position in a list of links towards being the material the answer is assembled from.
No. Much of the foundation is shared: crawlability, clear structure, credible sourcing and content organised around real questions serve both. Organisations with solid technical SEO usually need targeted additions rather than a separate programme. Where the two diverge is emphasis, since generative engines reward factual precision and extractable structure more than persuasive framing.
Less precisely than search analytics. The workable approach combines periodic manual checks of a fixed query set across several assistants, referral and crawler signals in server logs, and brand-mention tracking. Run the same queries on the same cadence so movement is comparable, and expect the picture to be directional rather than exact.
Specifications published as labelled HTML tables with units rather than as images or PDF-only downloads; application, material compatibility, standards met and approvals stated in plain sentences; one stable canonical page per product instead of a filter URL variant; and structured data so the machine reading matches what a visitor sees.
It varies by provider, since each refreshes its underlying data on a different cycle. Changes in how an assistant describes a company can appear within weeks of clearer structure going live, with a fuller picture over several months. That is often quicker than ranking movement, and it is less predictable and harder to attribute.
No. Citation and inclusion are decided by each provider’s own system and change without notice, so a guarantee would describe something outside our control. What we commit to is establishing the current position across the assistants that matter to your buyers, improving the structural factors within reach, and reporting what moves.
Tell us the questions your buyers ask and we will check what the assistants return now, and what would change it.