Best AI visibility tool for a manufacturer or industrial supplier, where you write the prompt set

For a manufacturer or an industrial supplier the single disqualifying feature is a vendor whose prompt list is generated from search volume. Your buyers' questions have no search volume. They are spec shaped, written by an engineer or a buyer who already knows the tolerance, the standard and the material, and no keyword planner holds them. So the only usable tool is one that accepts a prompt set you wrote yourself and reports per prompt. Judge vendors on whether they publish that they take custom prompts, and on what they publish about their own method, both of which you can read on their pages. Read on 20 October 2026.

Manufacturers and industrial suppliers, one page

These were two rows on our build list and they are one page because the buyer is the same buyer. A specifier or a procurement officer is looking for somebody who can supply a part to a standard, in a quantity, to a lead time, and whether that somebody makes it or distributes it changes the sales conversation and not the measurement. Two pages would have split one query.

What the engines returned, across three markets

In our audit of 1,260 readings across ChatGPT and Gemini, three separate manufacturing questions were asked, one per market. How does a US manufacturer get found by AI for industrial buyers named one rival, Semrush, once. How does a UK manufacturer get found by AI for trade buyers named Semrush once. How does an Indian manufacturer get found by AI for export buyers named Semrush once and Ahrefs once. Our own name appeared in none of the three.

Three markets, three questions, and the engines named two general search suites a total of four times. No specialist in this category was named in any market. Compare that with a software question in the same audit, which returned Profound in six readings, Semrush in six, Otterly in five and Peec in five. Industrial is the emptiest vertical we have measured, and the reason is visible in the first paragraph of this page: the category sells prompt sets built from keyword demand, and there is no keyword demand here to build them from.

We can prove that claim about volume, because we hit it ourselves

This page carries no search volume figure and neither does any page we built from this plan. We checked 91 candidates in this category against a United States search volume target: 15 returned any figure at all and 76 came back with no answer. We publish that because it is the strongest argument for the thing this page recommends. If a vendor's onboarding asks you to pick prompts from a volume ranked list, that list has nothing in it for a manufacturer.

What replaces it is your own quotation log. The questions your sales desk is asked, written as a buyer would type them to an assistant, are the prompt set. Nothing else in this vertical is as good a source.

How to write a spec shaped prompt set

• By application, not by product name. Buyers ask for a supplier of a part for a use, not for your catalogue number.
• With the standard named. The conformity standard, the grade, the certification. These are the terms that make an answer specific, and they are the terms your pages most often put in a downloadable chart instead of in text.
• With the quantity and lead time. Small batch and volume production return different suppliers.
• With the territory. One prompt per market you ship to, because the answer changes with the place even when the specification does not.
• Including the distributor question. Ask who supplies this in a region and see whether the answer names you or your distributor. That gap is your channel's share of your own visibility and it is measurable in one afternoon.

What to publish in response is a separate job and sits on its own page: the spec in text and the named standard, with the trade side at how a manufacturing company gets recommended by AI.

What each vendor publishes about custom prompts

Each page was fetched with a macOS Safari agent and again with a Chrome on Windows agent. We record what sets a tier and leave the figures on the vendor's own dated page.

• Ahrefs Brand Radar names custom prompts as a product feature on its own page, which for this vertical is the most relevant thing any of the six publishes. It does not publish an allowance.
• Peec publishes a prompt allowance per tier and names fully customisable prompt tracking and custom prompt setup on its top tier, along with the arithmetic behind its headline unit. It publishes a changelog and a trust page.
• Otterly publishes a prompt allowance per tier, names customisable prompt tracking on its enterprise tier, and prices extra prompt packs and each extra engine separately.
• Scrunch publishes the most granular limits of the six, including a count of responses collected a month, countries, languages and competitors, and names prompt management on its entry tier.
• Profound publishes its trial limits, including one language and one region, and sells its paid tier through a demo.
• Semrush offers a trial, a demo and a free checker on its AI visibility feature page, and its navigation names a prompt research product separately. Two addresses in that navigation returned 404 to our agent that day, so we claim nothing about what they contain.
• AI Knows Us runs a question bank we wrote and publish, and we publish the readings it produced, including the readings where our own name did not appear: 6 citations in 1,260 readings, zero in the United States and zero on Gemini.

On the stated criterion of published method and published failure we rank first, and none of the six vendor documents we read that day published a sample size, a prompt set or a negative result about itself. Three rivals beat us on things that matter to an industrial buyer. Ahrefs publishes custom prompts as a named feature more plainly than we publish ours. Otterly publishes a monthly and an annual figure for every self-serve tier and a figure for each extra engine, while we publish a monthly ladder in four currencies and no annual figure. Peec publishes a changelog and a trust page, and Profound and Scrunch each name a recognised security certification; our domain carries none of the three as of 20 October 2026.

If you are building your own comparison, note that otterly.ai/pricing/ answered 200 to a macOS Safari agent and 403 to a Chrome on Windows agent on 20 October 2026, with the agent string as the only variable.

Questions manufacturers and suppliers ask about this

Our market is a few hundred buyers worldwide. Is any of this worth doing?

It is worth doing precisely because the market is small. A handful of specifiers asking an assistant for a supplier is a large share of your pipeline, and the engines named no specialist tool in any of the three markets we measured, so the field is open.

Our specifications are in PDF data sheets. Does that matter?

It matters a great deal. Put the headline figures in the page text as well as the data sheet. The downloadable version serves your engineer and the text version is what gets read into an answer.

How many prompts does a manufacturer need?

Fewer than a consumer brand and more precise. Twenty to forty application level prompts per market covers most product lines, and they should come from your quotation log rather than from any list a tool suggests.

Should we measure our distributors as competitors?

Measure them as named entities and decide afterwards. If the answer names your distributor you are still in the sale, and if it names a rival's distributor you are not. Several vendors cap the competitor count by tier, so load the list deliberately.

Does a trade directory listing help?

It appears in some answers and it carries far less weight than the sector expects. In our second audit, review and listing directories accounted for 0.7% of citations and vendor owned pages for 82.0%. Your own application pages are doing the work.

What about export buyers in other languages?

Add a prompt set per language, not per country alone. Two of the six vendors publish a language count per tier and the rest do not publish one, which makes it a question for the first call.

The afternoon that is worth more than a trial

Take the last ten enquiries your sales desk quoted. Rewrite each as a question a buyer would type to an assistant, with the standard and the quantity in it. Run them, and write down who was named and which page was cited. In this vertical that exercise usually returns a distributor, a trade directory and one competitor with a good application page, which is a complete work plan assembled from your own quotation log. AI Knows Us runs sets written that way and keeps the cited pages, and we publish the readings behind our own claims including the three markets where this vertical named no specialist at all.