Key takeaways
- We ran our live AI visibility audit tool against 23 industrial manufacturing domains and could scan 13. Average visibility was 66.2% across 65 live, grounded buyer queries, and the median was 80%. Ten domains, 43%, blocked our crawler.
- Four companies were named in all five queries and four more in four of five. Three were named in one query or none, and one, Standex, was not named at all.
- Category and comparison questions went best (73.1% and 76.9%). Local-style and problem-first questions did worst, at 53.8% each. Buyers describing a failure or searching near them were the least likely to get a named manufacturer.
- AI drew on technical and reference sources: Wikipedia was 35 of 520 citations, with US patent publications (18), Thomasnet (13) and SEC filings (13) next. A manufacturer's own website was only 4.0% of what was cited.
- The three on-demand manufacturing platforms averaged 80%, against 62.0% for the other ten. For custom-part questions, the platforms mostly named and cited each other.
- This sample is mostly large public industrial brands plus three platforms, chosen by us. It likely flatters mid-size and family-owned manufacturers, and says nothing about small shops.
Why we audited manufacturers
Industrial buying is slow and technical. An engineer writes a spec, a buyer qualifies suppliers, and an RFQ goes out to a short list. Reputation, certifications and decades of work matter, and most manufacturers assume the right supplier gets found because of them.
Some engineers and buyers may describe a part, a standard or a failure to an AI assistant and read the suppliers it names, so we wanted to see what comes back. So we asked a simple question. When someone describes a manufacturing need without naming a company, which manufacturers get named, and are the big, established ones always on the list?
66.2%
average AI visibility across 13 industrial manufacturers, measured by live grounded search on 65 buyer queries
Deligatr Manufacturer AI Visibility Audit, October 3, 2026
That is much higher than we saw in our law firm audit, and it is the most misleading number in this post if read alone. Small sample, skewed toward large public brands, and 10 of the 23 sites we tried blocked our crawler. Treat it as a snapshot of this list, not a benchmark for manufacturers. We say more about the sample below.
How the audit works
The tool reads each company's homepage, classifies it in the words a buyer would use, writes five buyer-intent queries (one local-style, two category, one problem-first, one comparison) and runs each as a live, grounded AI search with the company's own name left out. It is one run, five queries per company, on one day. AI answers change between runs, so read every score as a snapshot. The full method is in our audit documentation, and all twelve industry audits are on our AI visibility research page.
A company is either in the answer or it is not.
The distribution: a strong top, and a tail that matters
Companies by AI visibility score
13 industrial manufacturers, each tested on 5 live grounded buyer queries. Number of companies at each score.
Deligatr Manufacturer AI Visibility Audit, October 3, 2026
Donaldson, Gates, Protolabs and Xometry were named in all five queries. Chart Industries, Graco, Timken and Woodward were named in four. These are well-known names with deep technical content, and they were named often.
The tail is the story. Hubbell and IDEX Corp were each named in one query, and Standex was not named in any. These are not small or obscure businesses. They are established, publicly listed manufacturers with long histories, and a famous name did not guarantee a place in the answer.
The platforms and the rest
Three of the 13 are on-demand manufacturing platforms or services: Protolabs, Xometry and Fictiv. Their average was 80%, against 62.0% for the other ten. Two of the four perfect scores were platforms.
One plausible reason is that these companies describe themselves in plain, answer-shaped language close to how buyers ask ("custom parts," "instant quote"). Another is that their questions were about custom parts and prototypes, where the same few platforms and sourcing sites kept being cited.
The comparison is not clean. The platforms were also tested on different questions from the component and equipment makers, and Fictiv scored only 40%. We note the pattern and do not claim to explain it.
What kind of question loses the most
How often a company was named, by query type
Share of queries in which the company was named. 13 companies, 65 queries.
Deligatr Manufacturer AI Visibility Audit, October 3, 2026
Comparison queries named a company 10 times out of 13 (76.9%), and category queries 19 out of 26 (73.1%). Both are the "top companies for X" and "X manufacturer" questions, which many roundup and list sites in the citations were written to answer.
The weaker results were at the two ends of a buyer's journey. Local-style questions, where the buyer wants someone nearby, named a company 7 times out of 13 (53.8%). Problem-first questions, where an engineer describes a need or a failure, did the same: 7 out of 13.
Those are the questions a mid-size manufacturer is likely to need most. A buyer who already knows to search for a top-ten list is a late-stage buyer. The earlier question, "we have this problem, who makes the right part," is where a smaller supplier can be found, and it is where even this large-brand sample slipped.
Who got named instead, and where the answers came from
Among the ten non-platform companies, the alternatives AI named were mostly large, established manufacturers: Lincoln Electric and SKF were each named four times, Regal Rexnord, Siemens and NSK three times, and Honeywell, Parker Hannifin and Linde Engineering twice. Three of those, Lincoln Electric, Regal Rexnord and Parker Hannifin, are among the domains our crawler could not scan, so they were named in other companies' answers while we could not test their own.
For the three on-demand platforms, the alternatives were mainly each other: Xometry, Protolabs, Fictiv and RapidDirect. None of those four platforms was named as an alternative in the other ten companies' answers.
The sources were broad. Across 520 citations on 338 domains, 278 domains were cited only once. A few repeat names stood out.
- Wikipedia was the largest single source, at 35 citations (6.7%).
- US patent publications (image-ppubs.uspto.gov) were cited 18 times, and SEC filings 13 times. Both come from large public companies with long paper trails.
- Thomasnet, the industrial sourcing directory, was cited 13 times in answers for five different companies.
- List and market-research sites such as Verityrank, Metoree, Inven, MarketsandMarkets and Verified Market Research appeared repeatedly.
4.0%
of all citations pointed to the company's own website (21 of 520), measured by exact match on the company's domain
Deligatr Manufacturer AI Visibility Audit, October 3, 2026
Nine of the 13 companies had at least one citation to their own site, and the rest had none. The own-site pages that were cited are instructive. Two were technical explainers: a Donaldson article on what a baghouse is, and an ESAB University guide to choosing welding consumables. Most others were product, category or home pages.
Distributors also showed up. In ESAB's answers, three welding stores (Linde Direct, Arc Captain and YesWelder wholesale) were each cited three times. In Hubbell's, one infrastructure-hardware distributor, TESSCO, was cited four times. When a reseller's catalogue is clearer than the maker's own site, the reseller can be what AI quotes.
The ten we could not scan
Ten of the 23 domains we tried, 43%, could not be audited: Altra, AMETEK, Enerpac, Helios, Kennametal, Lincoln Electric, Nordson, Parker, Regal Rexnord and Thermon. Our crawler was shown a bot-verification page or an error instead of the homepage.
That is a notable share, and what it means is limited. We cannot tell from outside whether the same protection also stops the crawlers AI assistants use. If it does, the company's content is not being read at all. If it does not, there is no problem. The only way to know is to test it.
Classification notes
The tool labels each company from its homepage, and a few labels are worth flagging. Xometry, Protolabs and Fictiv are on-demand manufacturing platforms or services, not conventional plants. Standex was read as an "industrial manufacturing conglomerate," and the queries generated for it were scattered across unrelated products, including electronics forming, aerospace components and a local search near its headquarters. That is a limit of how the tool reads a diversified homepage, not a verdict on the company.
What to do with this, if you run a manufacturing business
- Write the buyer's question, not the part description. Add application notes and process comparisons: which process fits what, at which volume, with which limits. Have your own engineers review them.
- State capabilities precisely. Materials, tolerances, volumes, processes and the industries you serve, said the same way on your site and in every directory. Vague claims such as "precision manufacturing solutions" are hard to match to a query.
- Treat certifications as evidence, not decoration. State only what you hold, with the right scope, matching the public registry. Remove anything that has lapsed.
- Check the sourcing directories. Compare your listing in Thomasnet and the industry lists that matter to your sector. Inconsistent or missing entries make you hard to place.
- Look at how your distributors describe you. If their pages are clearer than yours, brief them or fix your own.
- Test whether your site can be read, then re-test regularly. AI answers change, so a single score tells you little.
Because sales cycles are long, the buyer universe is small and the qualification process is slow, many manufacturers also benefit from pairing this with direct outreach to a named list of target accounts, which is what our Full Hybrid package combines in one CRM record. If you want the inbound work done as a program, see our AEO, GEO and SEO service for industrial manufacturers.
Book a free consultation and we will run your own constructed questions against live assistants and show you who is named instead of your company. Inbound is $2,500 a month on monthly billing.
Book a consultationEvery company we audited
Company | Visibility | Appeared on | Top rival named instead |
|---|---|---|---|
| Donaldson | 100% | Local, Category x2, Problem, Comparison | Camfil |
| Gates | 100% | Local, Category x2, Problem, Comparison | Regal Rexnord |
| Protolabs | 100% | Local, Category x2, Problem, Comparison | Xometry |
| Xometry | 100% | Local, Category x2, Problem, Comparison | Protolabs |
| Chart Industries | 80% | Local, Category x2, Comparison | Cryofab |
| Graco | 80% | Local, Category x2, Problem | Alfa Laval |
| Timken | 80% | Category x2, Problem, Comparison | SKF |
| Woodward | 80% | Local, Category, Problem, Comparison | Moog Inc. |
| ESAB | 60% | Category x2, Comparison | Lincoln Electric |
| Fictiv | 40% | Category, Comparison | Xometry |
| Hubbell | 20% | Category | Schneider Electric |
| IDEX Corp | 20% | Comparison | Crane Engineering |
| Standex | 0% | None | Onvio LLC |
Not scanned (bot-verification page or error): Altra, AMETEK, Enerpac, Helios, Kennametal, Lincoln Electric, Nordson, Parker, Regal Rexnord, Thermon.
Check your own number
Every company above was run through the same tool below. It takes about ninety seconds, and your company's name is never in the query.
Do AI assistants recommend you?
Five buyer-style queries for your category, run through live AI search, show who gets named instead of you. Takes about ninety seconds.
Free. The full report arrives by email so the tab can close.
Methodology and honest limitations
What we did. On October 3, 2026 we ran the live audit tool that powers our free checker against 23 industrial manufacturing domains: one run, five queries per company, visibility measurement only. The step-by-step method is in our audit documentation. A company that disputes its result can ask us to re-run it.
The sample. We chose real, identifiable companies, mostly large public industrial brands plus three on-demand manufacturing platforms. It is a sample we picked, not a random one and not a census. It likely flatters the average for mid-size and family-owned manufacturers, because small and mid-size suppliers are barely in it. A company's score also depends on how our classifier read its homepage, and on 13 companies, one result moves the average noticeably.
What it does not cover. Only the homepage was read, so a company with a thin homepage and strong inner pages may be undercounted. Five queries per company is a small sample of real buyer language, and we did not test part-number searches, RFQ behavior or any certification claim. Results come from one run on one day, and AI answers change over time. We did not test whether any company pays for directory placement.
On export controls and certifications. Nothing here is legal advice. Certification, origin-claim and export-control rules depend on a company's products, customers and jurisdictions, and your own compliance counsel should make the final call.
FAQ
How visible are industrial manufacturers in AI answers?
Among the 13 we could scan, quite visible: average visibility was 66.2% across 65 buyer queries, and eight of 13 were named in at least four of five. But this sample is mostly large public brands plus three on-demand manufacturing platforms, so it likely flatters mid-size and family-owned manufacturers. Three companies were named in one query or none.
Why did some well-known manufacturers score so low?
We cannot see inside the AI, so we can only describe what happened. Standex, which our tool read as an industrial manufacturing conglomerate, was named in none of its five queries, and Hubbell and IDEX Corp were each named in one. For a diversified group, a homepage that spans several businesses may make it harder to place in any one category.
Where does AI get its answers about manufacturers?
From a wide spread of sources. Across 520 citations on 338 domains, Wikipedia was the largest at 35, followed by US patent publications (18), Thomasnet (13) and SEC filings (13). Industry list and market-research sites also appeared repeatedly. A manufacturer's own website was 4.0% of what was cited.
Why did so many sites fail to scan?
Ten of the 23 domains we tried, including Lincoln Electric, AMETEK and Parker, showed our audit crawler a bot-verification page or an error. Large industrial sites often run heavy bot protection, and we cannot tell from outside whether it also stops the crawlers AI assistants use.
Does this tell me how a small machine shop or contract manufacturer would do?
No. We did not test small shops, job shops or family-owned manufacturers. The companies we could scan are far better known than the typical supplier, and a smaller manufacturer is likely to be named less often on generic queries. Only a test of your own site tells you.
Do certifications like ISO 9001 or AS9100 make a manufacturer more visible?
Our audit did not measure that, so we make no claim from it. Certifications are signals a buyer checks during supplier qualification, and they should be stated accurately, current and matching the public registry. Stating one you do not hold, or one that has lapsed, creates risk instead of visibility.
