For industrial distributors
When a plant buyer asks ChatGPT or Perplexity “which specialty-chemical distributors in [region] stock [process] consumables for [application]”, the model returns a shortlist built entirely from third-party editorial — a trade-pub distributor roundup, a Thomasnet category listing, a bylined distributor-expert interview, a co-published case study. The distributor’s own product pages are not what the model cites. Thezeuz earns the distributor’s named placement on the source pages the model already reads, so the pre-shortlist line is the distributor’s name.
The pre-shortlist problem
Distributor-shortlist queries — specialty-product distributors, regional MRO stockists, process-consumables suppliers — run on a small set of third-party pages the engines already index. The line below is what the buyer reads first when they prompt the AI for a supplier near their plant.
Read a worked example →The prompt a plant buyer runs
“Which specialty-chemical distributors in the upper Midwest stock [process] consumables for [application] at a [tier-one OEM]”
What the model returns
The source-page set
The four categories below are the source pages a distributor-shortlist query lifts from. Trade-pub distributor roundups, Thomasnet and Engineering360 listings, bylined distributor-expert interviews, and co-published case studies — each one named by the editor who runs it, each one read by the model before the buyer ever loads the distributor’s site.
Trade-pub distributor roundup
Trade-publication features in Industrial Distribution, MDM, Supply House Times, and Thomas Industry Update — the roundups, regional supplier lists, and category features that name the distributor alongside a regional cohort on a durable URL. Perplexity cites the roundup as the footnote for the buyer’s `who supplies X near Y` prompt before the distributor’s own page ever loads.
Thomasnet / Engineering360 listing
Thomasnet category listings, Engineering360 GlobalSpec product directory entries, and the regional industrial-association supplier index — each one a structured page that names the distributor with its category, geography, and line-card spelled out. The model lifts the distributor’s name and capability strings from these entries verbatim — phrased in the buyer’s category-prompt language.
Bylined distributor-expert interview
A trade-publication interview with the distributor’s principal or category lead — on-record in Industrial Distribution, MDM, Supply House Times, or the vertical equivalent — with the distributor’s name, region, and specialty line attached. The interview page is the durable URL the model lifts when the buyer prompts for a recommended supplier.
Co-published case study
A referenceable case study co-published with a named end-customer — the plant, OEM, or MRO shop whose outcome the distributor actually delivered against — published on the trade-pub’s site with both names on the page. The model reads co-published case studies more readily than vendor marketing because the end-customer is on the record.
The coverage plan
The intake opens with four fields — company, vertical, deal-size band, and current AI-search standing — and the coverage plan names the trade publications, supplier directories, expert outlets, and referenceable case studies the work will run against. Below is what each re-read of the model reads for.
What the coverage plan names, in plain language.
One intake at a time
Send four fields — company, vertical, deal-size band, and current AI-search standing — and Thezeuz replies within one business day with the trade-pub, supplier-directory, expert-outlet, and referenceable case-study coverage plan for the surfaces ChatGPT, Perplexity, and Google AI Overviews already lean on for the buyer’s `who supplies X near Y` query.