A thread on r/procurement asked how you source a product you've never bought, and anyone selling industrial goods should read the replies. They ranged from customs data to licensed industry databases, but one practitioner described a routine worth studying. They ask Perplexity for manufacturers holding a given certification, then confirm them with freight forwarders, because a directory can't verify export paperwork.
How do you find new suppliers when sourcing a new product/material?
How do you find new suppliers when sourcing a new product/material? Let’s say you need to source a material or product that you haven’t purchased before. How do you go about finding new suppliers? Do you: Search for the product/material on...
Notice who does what. Software drafts the longlist, a human vets it, and any manufacturer the engine left out never hears that an RFQ went out. That's industrial marketing in 2026, compressed into one sourcing habit.
The goal hasn't changed: be easy to find when a buyer starts looking. That moment now happens in a chat window, where a typical manufacturer's website, designed for an earlier web, is effectively invisible. Below are five plays ranked by return, then how they change by business type, and how to tell whether they work.
Why industrial sites are unusually invisible
A manufacturer starts with three handicaps a SaaS company doesn't have, and once you see them, the plays suggest themselves.
First and biggest, your numbers live in PDFs. Buyers filter on tolerances and certifications, on temperature ranges and MOQs, and all of it sits in datasheets, spec sheets or capability brochures, formats that retrieval systems struggle with and models hesitate to quote. Your website offers a "download our datasheet" button, but the engine needed row three of that datasheet as HTML, so it cited a rival whose site had it.
Then there's the copy. A phrase like "World-class precision manufacturing solutions" could describe any machine shop anywhere, so a model working out which suppliers can machine titanium to aerospace tolerances gets nothing it can quote. Industrial brochures speak in adjectives, and answer engines want facts someone could filter on.
The third handicap is technical, and accidental. Plenty of these sites predate AI search, with PDFs gated behind forms and firewall rules an old security review cranked up. No one has tested whether retrieval bots can reach the product pages, because the test didn't exist. We see the access gate break silently here more than in any other vertical we look at. 3PLs, forwarders and carriers hit the same problem, which our logistics playbook covers.
Luckily, your competitors share all three problems. Rival shops and component makers have the same PDFs, brochure copy and unchecked firewalls, so the race for AI visibility in this vertical has barely begun. Nobody's bidding on these plays yet, which keeps them cheap, and in industrial markets an early infrastructure move pays for years.
The five plays
Play one returns the most per hour: move the specs out of the PDFs. For each main product line, put the datasheet rows that decide purchases on the product page as an HTML table, covering materials and tolerances, certifications and operating ranges, and MOQs with lead-time bands.
The PDF can stay for engineers, and the table serves the assistants that brief them. When a small supplier with a plain HTML catalog beats a far bigger one with gorgeous gated brochures in AI answers, this play is usually why.
Application answers are the second play. Engineers bring compatibility questions to assistants, such as whether a coating survives salt spray, whether a pump tolerates running dry, or which connector suits a particular standard. Answer each one in its own note, verdict first and conditions second. That format works as citation bait for manufacturers, because it mirrors how engineers phrase the question, and hardly anyone in industrial markets puts such notes on the open web.
For the third play, rewrite the capabilities page as the checklist a sourcing filter would run. Replace brochure prose with certifications, giving the scope and expiry date for each, and with your equipment and the envelope sizes it handles. List the industries you serve with their actual named constraints, plus your capacity ranges. Think back to the Perplexity user: they filtered on certification, and the suppliers who surfaced had theirs written out as text an engine could lift.
Next is the listing layer. Your brand also lives on distributor pages, marketplace listings and industry directories, many of which rank above your own site, and engines compare every one of them with what you say.
Any disagreement, whether a spec, an outdated product name or an ISO cert a directory claims and your site never mentions, gives a model grounds to hedge or drop you. An afternoon each quarter getting your biggest listings to match on specs, names and certs is off-page work that pays unusually well in this vertical.
The last play is opening the gates deliberately. Go through robots.txt and your CDN's bot settings, testing each retrieval crawler by name, and confirm the product pages still show their facts with JavaScript switched off. After that, search a month of server logs for the fetchers that pull pages at answer time. Industrial sites fail this check more often than they pass it, and one firewall rule tends to fix it.
How often does ChatGPT mention your brand?
Most founders have no idea. The answer might surprise you.
One plant, worked
Picture an illustrative precision machining shop with 80 employees. It holds aerospace and medical certifications, and nobody has rebuilt its website since 2019.
I'd run play five first, because it's the diagnosis. Searching the logs turns up PerplexityBot hitting a challenge page, thanks to a firewall rule added during a 2023 security audit. After a single allowlist entry, the retrieval bots can finally reach the site.
Play one comes next: the six product families the shop quotes most each get an HTML spec table above the PDF link, listing materials and tolerance classes, envelope sizes and certs. Play three then swaps the capabilities page's "precision solutions for demanding industries" paragraph for plain facts: the two ISO certs, each with its scope and expiry date, the envelope of the five-axis machine, a list of materials, and the industries served with their named constraints.
Over the quarter, play two produces four application notes, each built around a question the shop's sales engineer gets asked monthly. Note one covers titanium versus stainless, which comes up at the start of half their aerospace calls. Play four needs one afternoon. Three directories rank above the shop's own site, and cleaning up those listings turns up one that still displays a certification scope two renewals out of date.
Tracking uses eighteen queries shaped like real sourcing, checked weekly. Results in shops like this tend to come in a set order. Identity and capability queries move first, within weeks, since so few suppliers compete for them. Supplier queries filtered by certification come next, once the corrected directory listings start backing up what the site says. Eventually a quote request arrives saying "found you through ChatGPT," usually after enough delay that the skeptics fall silent when it's read out in a meeting.
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The same playbook, four business types
If you make components, most of your gains will come from plays one and two. Your buyers screen suppliers on specs and ask about compatibility, and each datasheet row you leave inside a PDF hands a query to someone else.
For equipment OEMs, play two tilts toward application and ROI questions, and comparisons count for more. You need an honest page comparing the options in your equipment class, criteria spelled out and trade-offs admitted, which is the page engines quote when a buyer asks which machine to purchase.
Contract manufacturers sell capability itself, which makes play three their moat. Put your envelope sizes and materials, your certifications and the industries you serve into text an engine can lift, and you'll be named when someone asks which shop can build their part. A shop that describes itself as "precision solutions" won't be.
Distributors are the aggregators here, which helps, since engines favor anything shaped like a catalog. Aim for coverage and accuracy, with complete, filterable line data kept current, so engines cite you when buyers ask what's available. You can then sell the consistency work described above to the lines you carry, as a service.
What not to do, briefly
Two moves tempt industrial marketers, and both waste money. The first is hiring a generalist agency for an "AI visibility audit" before doing play five and your own query baseline. You can run that diagnosis in an afternoon, and the buyer's guide to that market explains how walking in with your own numbers changes the conversation with any vendor.
The second is answering all this with a chatbot on your website. What counts is the assistant your buyer already uses, so feed it good material instead of building a rival. The budget for those two moves would pay for all five plays here and leave some over.
Measuring in a long-cycle business
Your query set should mirror real sourcing rather than SaaS habits. Include certification-filtered supplier queries (ISO-certified X manufacturers, exporters of Y with Z cert) and capability queries (who can machine A in B at C tolerance). Then add play two's compatibility questions, plus checks on your company name and your brands.
Check the set weekly in ChatGPT, Perplexity, Gemini and Google's AI surfaces. Record which answers cite you and, even more importantly, how they describe you. If an engine states your cert scope or your MOQ incorrectly, it's silently filtering you out of longlists. You can reuse the six-engine tracking system unchanged apart from the queries.
Expect a lag, and warn leadership early. RFQ cycles take months, so your first signals are showing up in answers and being described correctly. Mid-funnel proof arrives when the "how did you find us" field on quote requests starts naming AI assistants, and revenue proof comes a sales cycle later.
Early movers see what we see in every vertical we track: a small number of unusually qualified visitors who arrive with the longlist already built. The manufacturers on it had facts the engines could read a quarter earlier. This industry has always been about being one of three names on a quote, and the answer layer is just the newest place those names get picked, with a spec table in HTML as the price of entry.
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