Industrial & Automotive · India · AEO / GEO

Getting industrial brands named by AI search

When buyers ask an AI assistant to shortlist suppliers, the shortlist is assembled from whatever the model can confidently attribute. For JBM and Metso, that now includes them. This is what we did, and what we can and cannot prove about it.

The problem: invisible to the shortlist

Industrial buying has moved earlier. Procurement teams and engineers now open with a question to an assistant — "who makes electric buses in India", "which companies supply mining crushing equipment" — long before they reach a vendor website.

That shortlist is not the top ten organic results. It is assembled from sources the model can resolve and quote with confidence. A brand with a strong website can be entirely absent from it, and never know.

What we did

• Resolved the entity first. Complete Organization markup, consistent naming, and corroborating profiles that agree with each other. A model that cannot confidently identify a company will not name it.
• Published specifics, not adjectives. Capacity figures, market share, certifications, applications, part-level detail. "Leading manufacturer" cannot be quoted; "20,000 electric buses annually" can.
• Made pages extractable. Question-shaped headings, real hierarchy, lists over dense prose, and structured data matching what a human sees.
• Built depth where the category is decided. Model, application and specification pages carrying the language buyers actually use, rather than the language the brochure uses.

What the assistants now say

The detail in the ChatGPT answer is the part worth noticing. Installed capacity and market share are specifics that only exist in an answer because they existed, in extractable form, on pages the model had read.

What this does not prove

We would rather state the limits than overclaim, because most AEO marketing does the opposite.

These are captures from specific queries on specific days. AI Overviews regenerate, vary by location and by searcher, and change as the underlying results change. A citation is not a position anyone holds. We also cannot attribute revenue to a citation the way a click can be attributed — the measurement layer for this simply does not exist yet.

What can be said is narrower and still useful: the pages behind these answers became extractable enough to be quoted, and that is the part a marketing team can actually control.

The transferable lesson

Every citation here rests on the same foundation: a resolvable entity, published specifics, and structure a machine can lift from. None of it required guessing at how models rank. It required making the underlying facts unambiguous — which also happens to improve conventional search performance, so the work is not a bet on one channel.

Sources: Google AI Overview and ChatGPT responses captured 27–28 August 2026 from an India location. Competitor entries in the screenshots are blurred. Assistant answers regenerate and vary by searcher; these captures are evidence of extractability, not of a permanent ranking position.