Geongyn — Generative Engine Optimization for building products
Contractors now ask ChatGPT who makes it, who stocks it nearby, and which gauge to use. We engineer building-product manufacturers and suppliers into that answer.
A contractor pricing a barn roof used to flip through binders or call three suppliers. Now he types the question into ChatGPT and gets back two or three names.
Those are the companies that get the call. Everyone else was never in the conversation — and never found out there was one.
For standing seam panels in Ontario, a few manufacturers come up most often — the ones whose profiles, gauges and dealer networks are documented clearly enough to compare.
Nobody calls to tell you they specified someone else. The quote requests just thin out. You put it down to interest rates, or the season, or everyone building less this year.
So run the number that actually matters — and it isn't one order. It's one contractor. Take what a steady account orders from you in a year, then multiply it by how long those relationships usually run.
1contractor
accountwhat they order
in a year5years of
reorders
That's the size of one decision you never watched being made. It takes about four seconds, inside a chat window, while somebody else's spec sheet gets read out loud.
Thirty years of quality doesn't travel through a PDF. When your gauges, spans and finishes only exist inside a brochure — or behind a request-a-quote form — there is nothing for an assistant to read, quote or compare you on.
So it names the competitor whose numbers were sitting there in plain text. Not because they build better. Because they were legible.
What AI reads instead
Readable spec data
Gauge, span, finish, warranty — as text on a page, not buried in a PDF.
Structured product records
Each product described in a format a machine can actually parse.
A catalogue it can see
The full line, in detail, without "contact us for pricing."
Where to buy
Who stocks or installs your product, and in which region.
Trade corroboration
Association listings and industry directories that vouch for you.
Miss enough of these and you don't rank lower — you're not in the answer at all.
Six questions about your product data. The same six signals we score in a full audit. Your honest number out of 100 — no email required.
The six signals we score you on
Four steps, in this order, because each one depends on the last. There's no point chasing directory listings while your spec sheets are still locked in a PDF.
We run the questions your buyers actually type — who makes it, who stocks it, which gauge — through ChatGPT, Gemini, Grok and Claude, and write down who gets named instead of you.
Gauges, profiles, spans, finishes, warranties. We get them out of the PDFs and onto pages, in structured form, so a machine can quote your numbers instead of guessing at them.
One website saying you exist isn't enough. We work you into the trade directories, association listings and industry pages the engines cross-check before they'll name anyone.
We re-run the same questions every month and report what moved, who moved, and what changed in how the engines answer your category. Being the answer is rented ground.
We ran structured interrogations of four major AI engines — ChatGPT, Gemini, Grok and Claude — and cross-examined how each one decides which company to name.
They don't reward the best manufacturer. They reward the one they can read. For a product company that comes down to whether your specifications exist as text a machine can quote, and whether anyone outside your own website corroborates you. That's what we build, in that order.
4
AI engines interrogated
3
rungs of buyer question we test
6
signals scored and weighted
100
points in the number you get
Our own method, and our own interrogations — not borrowed industry folklore.
About Geongyn
Geongyn is a generative engine optimization (GEO) and answer engine optimization (AEO) agency based in Ontario, Canada. It works with building-product manufacturers and suppliers — metal roofing and steel building products, lumber yards and building-supply dealers, window, door, siding and cladding makers, and concrete, precast and masonry producers — across Ontario and the rest of Canada. Its clients' buyers are contractors, builders, architects and spec-writers who increasingly ask AI assistants which product to specify and which supplier to call. Most manufacturers are absent from those answers for a structural reason: their specifications live in PDF brochures, their catalogues sit behind "contact us for pricing," and no structured product data exists for an engine to read. Geongyn's work is to fix that — publishing gauge, profile, span, finish and warranty data as machine-readable pages, building where-to-buy and dealer coverage, earning listings in the trade directories engines cross-check, and re-testing the same buyer questions every month to report what moved.
One contractor who starts specifying your product doesn't place an order. They open an account.
Based in Ontario, Canada — working with manufacturers and suppliers across Ontario and the rest of the country.
It's the same audit we'd charge for. Free, because showing you what the engines say about your own company is a faster argument than anything we could put in a deck.
“Our customers are contractors. They don't use ChatGPT.”
The older ones mostly don't. The ones running the crews now look things up on their phone between jobs, and Google puts an AI answer above your listing whether anyone asked for it or not. You don't need the whole trade to switch — you just can't afford to be missing from the ones who already did.
“We sell through dealers, not off a website.”
Which is exactly why this matters. When somebody asks where to buy your product near them and there's no dealer page for an engine to read, it can't route them to your dealers — so it names a competitor who can be routed to. Your channel is the thing being made invisible.
“Everything is in our catalogue PDF.”
That PDF is the problem, not the solution. It's the single most common reason a good manufacturer is missing from these answers: the numbers exist, they're just in a format nothing can quote. Getting them onto pages is the highest-value work we do.
“We've been at this forty years. Everyone knows us.”
Everyone who already knows you does. A machine has no memory of your reputation — it only has what it can read this morning. Forty years of trust and a two-page website look identical to it, which is unfair and also fixable.
“We already pay someone for SEO.”
Keep them if they're working. This is a different job: SEO competes for a position in a list of links, and this decides whether you appear in an answer that replaces the list. Plenty of companies ranking well are nowhere in the AI response for the same query.
“How do I know this isn't snake oil?”
Don't take our word for it. Take the free audit — we run your buyers' actual questions, show you the answers, and name the competitors that came up instead of you. If we're wrong, you'll know inside twenty minutes and it cost you nothing.
Book the call. We run your buyers' real questions through the engines, score your product data out of 100, and hand you the whole thing — the questions, the competitors that came up, what's broken, and the order to fix it in. If we're not a fit, you keep all of it and we shake hands. If we are, you'll know exactly what you're buying before you're asked to buy it.
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FAQ
Generative engine optimization is the work of getting a company named inside the answer an AI assistant gives, rather than ranked in a list of links. When a buyer asks ChatGPT, Gemini or Google’s AI who supplies a product, the engine returns two or three names instead of ten results. GEO is how you become one of those names. Answer engine optimization (AEO) is the same idea aimed at structured answer boxes and voice assistants. Geongyn does both.
Make your product data readable, then get corroborated. In practice that means publishing your specifications — gauge, profile, span, finish, warranty — as text on web pages rather than inside PDF brochures, adding structured product data so each item is machine-parseable, building a where-to-buy or dealer page so regional questions can resolve, and earning listings in the trade directories and association pages engines cross-check before naming anyone.
Almost always because there is nothing for the engine to read. Manufacturer websites tend to keep their real product information in PDF catalogues, behind “contact us for pricing,” or in images of spec tables. None of that is quotable. The engine isn’t judging your quality and deciding against you — it never had your numbers in the first place, so it names the competitor whose numbers were sitting in plain text.
They’re the single most common reason a good manufacturer is invisible. PDFs are parsed poorly or not at all, spec tables inside them are often images, and nothing in them can be quoted cleanly in an answer. Keep the PDF — contractors still print them — but publish the same gauge, span, finish and warranty data as an HTML page alongside it. That one change usually moves more than anything else on the list.
SEO competes for a position in a list of links. GEO decides whether you appear in the answer that replaces the list. They run on different signals, which is why plenty of companies ranking well for a term are completely absent from the AI response to that same term. Traditional SEO leans on keywords and backlinks; this leans on whether a machine can read, quote and corroborate your product information.
That’s the case where it matters most. When a buyer asks where to buy your product near them and no dealer or where-to-buy page exists, the engine has no way to route them into your channel — so it names a manufacturer it can route to. Publishing dealer coverage by region makes your existing distribution findable instead of invisible, which is usually the fastest win available.
Manufacturers and suppliers whose buyers are contractors, builders, architects or spec-writers. In practice: metal roofing and steel building products, lumber yards and building-supply dealers, truss plants and engineered wood, window, door, siding and cladding makers, and concrete, precast and masonry producers. The common thread is a real product line, an owned domain, and customers who buy repeatedly rather than once.
The foundation work takes about 60 days, and movement in AI answers shows up in months rather than weeks. Some things shift quickly — once your specs are published as readable pages, engines can start quoting them almost immediately. Corroboration is slower, because directory listings and industry mentions accumulate. Anyone promising movement in three weeks is guessing. We re-run your tracked questions monthly so you can see the actual pace.
We scope it on the call and quote one upfront fee for the foundation work plus one monthly fee to hold the position. There’s no published package menu, because the right number depends on your product line, how much of your data needs rebuilding, and what a single contractor account is actually worth to you. You’ll see the full figure before you’re asked to decide anything.
No, and be suspicious of anyone who does. Nobody controls what an AI engine says on a given day, and any agency promising a specific answer is selling something they can’t deliver. What we do guarantee is the method, the work itself, and complete transparency about what we changed and why — plus a monthly report showing what moved and what didn’t.
They decide who gets the call. When an assistant returns two or three suppliers instead of a page of links, the shortlist is made before you know a buyer was looking. For a manufacturer the stake isn’t a single order — it’s whether a contractor starts specifying your product or a competitor’s, and that decision tends to hold for years of reorders.
Book a call
20 minutes. No pitch deck. You leave with the audit either way.