Buyers are asking an assistant before they reach your site.
An industrial AI search visibility audit establishing how answer engines currently describe your company, your products and your capabilities, which sources they cite when they do, and what on your own estate makes you citable or leaves a competitor to answer for you.
A procurement lead comparing suppliers or an engineer checking whether a material suits an application increasingly asks an assistant first and arrives at the site later, already holding a view. The audit establishes what that view currently is, which is a different question from where the site ranks.
Answer engines assemble responses from your site, from distributor and marketplace listings, from trade directories and association pages, and from older material that may no longer reflect your range. Where a third-party listing is the cited source, correcting that listing frequently changes the answer more quickly than changing your own pages does.
Extraction favours content that states things plainly and structurally: a direct answer near the question, specifications as data rather than as an image, a clear scope statement about what a product does and does not cover, and units and standards named explicitly. Much industrial content fails on presentation rather than on substance.
Performance data held only inside a PDF, or rendered as a picture of a table, is largely unavailable to extraction. Bringing the same figures onto the page as structured data, with the document alongside rather than instead, tends to be the single change with the widest effect on an industrial estate.
Answer engines do not expose a ranking mechanism, they change without notice, and outputs vary between users and sessions. We measure what can be observed, repeat the measurement on a consistent set of prompts, and report movement over time. Anyone offering a guaranteed position in an AI answer is describing something that cannot be verified.
A baseline of current answers across a defined prompt set, the sources being cited, a gap analysis against your own content, prioritised recommendations and a repeatable measurement method. See AI search visibility for industrial B2B for the ongoing programme.
Code Industrial is the industrial B2B practice of Code Barcelona, an agency building corporate websites and digital platforms since 2015. The same strategy, design and engineering team works on every industrial project, from the first scoping session through to life after launch.
The failure mode differs by sector. An industrial AI search visibility audit starts from the questions buyers ask.
What industrial companies ask about how assistants represent them.
How the major assistants currently answer the questions your buyers ask about your category, whether you appear, how your capability is characterised, which sources are cited, and which claims made about you are inaccurate. It also establishes why the cited sources are being used instead of your own pages, which is the part that indicates what to change.
No. Answer engines publish no ranking mechanism, change without notice, and produce outputs that vary between users and sessions. What can be done is to make your material easier to extract and cite, correct the third-party sources being used instead, and measure the result on a consistent basis over time. A guaranteed position is not something anyone can verify.
It overlaps and is not identical. Both reward clear structure and credible, specific content, and a site that is technically sound tends to do better at both. The difference is that extraction favours a direct answer stated plainly and data available as text, whereas classical ranking is more tolerant of information held in a document or an image.
It is usually the largest single factor on an industrial estate. Figures held only inside a document, or rendered as a picture of a table, are largely unavailable for extraction. Publishing the same data as structured page content, with the document alongside for those who need the controlled version, tends to change more than any other individual improvement.
That is a commercial decision for your organisation and it cuts both ways: blocking reduces the chance of your material being reproduced without attribution, and also the chance of being cited when a buyer asks about your category. We set out what each directive does and what it would mean for your visibility, then implement whichever position you take.
First establish where it comes from. If the source is a distributor listing, a directory entry or an old press release, correcting that source is usually faster than changing your own site. If it comes from your own content being ambiguous or outdated, the correction is to state the current position explicitly and clearly on the relevant page.
A baseline, then re-measurement at three to six months, is a reasonable rhythm for most industrial companies. More frequent measurement mostly captures session-to-session variation rather than real movement. The prompt set and scoring method are handed over so the repeat can be run by your team if you prefer.
It helps by removing ambiguity about what a page describes, which product it concerns and how specifications relate to it. It is a supporting measure rather than a mechanism for inclusion. Clear on-page statements of capability, scope and specification carry more weight, with structured data making those statements easier to interpret correctly. See schema markup for industrial websites.
AI visibility work frequently connects to these.
Buyers are asking assistants about your category before they reach your site. Tell us your products and markets and we will tell you how we would approach the industrial AI search visibility audit.