009 Agency is a manufacturing SEO agency built around Generative Engine Optimization: we make industrial OEMs, robotics and automation vendors, hardware makers and sourcing services visible, cited and chosen inside AI answers across ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek — and we re-measure it every two weeks against a logged baseline. Industrial buyers ask the engines the way they write an RFQ: a list of constraints, then “who should I shortlist”. The answer is a vendor list, and it is assembled before anyone visits your site.
Industrial is a three-audit bench for us: a warehouse-robotics vendor, a manufacturer of modular edge data centres, and a factory-sourcing service, measured across the US, UK, Germany and three Asian and CIS markets. Three is not many, and we say so. But the three contain the only case in our whole dataset where all four engines put the client first, and the reason it happened is the most useful thing on this page.
why industrial companies lose, and occasionally win, in AI answers
The general failure pattern holds everywhere: known but not recommended. In our published dataset 93% of brands were described accurately by AI and only 4.5% were named when a buyer asked who to pick. Industrial adds two problems and one genuine advantage.
you can be named on the shortlist and still lose the category
A warehouse-robotics vendor we audited was named on 15 of 24 prompt runs and cited on 8 — the best recall in our set for a company of its size. On the one question that mattered to it, “who leads climbing robots”, three of four verdicts went to a competitor, and ChatGPT stated flatly that the client’s robots “do not climb”. They do. The flagship product page never used the word. The engine cannot associate you with a category term your own site avoids, and a competitor who launched a rival product a fortnight earlier owned the word instead.
the constraint-rich prompt is where a specialist wins outright
A maker of prefabricated modular data centres for edge and AI inference — 2.5 organic visits a month, no Wikipedia, no Crunchbase — was shortlisted by all four engines on a prompt that listed the buyer’s real constraints (white-label, free cooling, edge AI, deployment in the EU, CIS and MENA), and ChatGPT and Claude ranked it first, citing its own site. On the generic “modular data centre manufacturer” question it did not exist; Schneider, Vertiv and Huawei did. Forty-one posts of three to six thousand words, genuinely deep, made the specific answer; nothing on the site made the generic one. Both are winnable, and they are won differently.
trade media and integrator roundups are the shelf, and your schema is not trust
Asked which goods-to-person vendors a mid-size US 3PL should shortlist, ChatGPT named AutoStore, Exotec, Geek+, HAI Robotics and Locus, and built the list from supply-chain research sites, an automated-warehouse trade title and a fulfilment marketplace. Perplexity added GreyOrange, inVia, OPEX, Swisslog and Dematic from integrator and robotics-directory pages. Not one vendor site was the reason a vendor was on the list. And in the sourcing audit, an organisation record that was textbook-perfect — legal name, address, founding date, five profiles — still produced “could not verify the owner, treat as unverified until you confirm the legal entity” from ChatGPT. Markup describes; third parties verify.
what the shelf looks like in our audits
| segment | what the buyer asked | what the engines answered |
|---|---|---|
| warehouse robotics | “who leads climbing robots”, plus 11 shortlist and comparison prompts in the US, UK and Germany | named 15/24, cited 8/24; the category verdict went to a rival 3 of 4 times; Germany 0 of 6. The root domain returned 403 to every AI crawler; the schema author was a leaked CMS account. |
| modular edge data centres | a constraint list (white-label, free cooling, edge AI, EU/CIS/MENA) and the generic category question | all four engines shortlisted the client on the constraint prompt, ChatGPT and Claude first; absent from Google’s AI Overview on the generic one. The engines also flagged, unprompted, that the site said founded 2015 and the schema said 2023. |
| factory sourcing, Asia | “a sourcing agent for villa fit-out from China” in four languages | The companies the engines named first had zero organic traffic; ChatGPT translated the Russian and Ukrainian prompts to English before searching, so a 95-page Russian corpus was never a candidate. Perfect schema, verdict still “unverified”. |
| goods-to-person automation (live, 5 sep) | “which vendors should a mid-size US 3PL shortlist” | ChatGPT: AutoStore, Exotec, Geek+, HAI, Locus, from supply-chain research and trade media. Perplexity: those five plus GreyOrange, inVia, OPEX, Swisslog, Dematic, from integrator roundups. No vendor site decided the list. |
what the first 90 days look like
The order is the same as everywhere, because it has to be: readable, then verifiable, then quotable.
- Audit. The specification-style prompts your buyers actually ask — constraints, categories, comparisons, per market and language — run live through the engines and logged verbatim, plus crawler checks with real AI user-agents on every domain you own. You get a health score, the shortlists engines currently give in your category, and the three most expensive gaps. Complimentary. what is inside →
- Own the category term. The product page says the word the market uses for it; one founding date, one entity, one canonical domain; the sitemap includes the posts that answer the specific questions; crawlers get the page, not a 403. Shipped as files your developer applies. technical GEO →
- Publish what gets quoted. Specification and comparison pages written the way a buyer writes an RFQ, and one page per generic category question you are currently absent from — in English first, because that is the language the engines search in.
- Get onto the lists. The trade titles, integrator roundups, research sites and directories the engines actually cited for your category — identified from your own panel, not from a generic outreach list.
- Re-measure. Same panel, every review, reported as a delta. Series, not screenshots.
proof, and its limits
The industrial bench is three audits and no client on retainer, so every number on this page is an audit measurement, not a program result. The measured result we can point to comes from another vertical: a legal client went from 0/5 to 4/5 engines naming it on its category prompt by day 17 (July 2026), with the log published. The edge-data-centre case above shows the same mechanics working without any program at all — deep, specific, readable content won the specific prompt on its own. What it did not do is win the generic one, and that is the work.
one thing we will not do
We will not invent a category claim. “Pioneered”, “first”, “leader” are checked by the engines against trade press and launch dates, and in the robotics audit the engine used a competitor’s launch announcement to decide who led. If you did pioneer the category, the work is making the record say so where the engines read; if the record does not say so, we will tell you before writing the word.