B2B SaaS · Canada and the United States
GEO for SaaS companies.
By Josh Redecop · Published
Generative engine optimization for a SaaS company means getting your product named when a buyer asks ChatGPT, Gemini, Perplexity, Claude or Google AI which software to use. Those answers come back as a short list of products, and a product that is not on the list does not get evaluated. The work is to find out which questions your buyers ask, see who gets named, and fix why it isn’t you: publish comparison, alternatives, category, integration and pricing pages a machine can read, mark them up with SoftwareApplication schema, strengthen the review sites and articles the engines cite, and keep your product described the same way everywhere.
When a buyer asks ChatGPT for the best software in your category, we make sure your product is on the list. We show you the real answers for your category before you buy anything, then we do the work and re-scan every month.
The five questions we track
Software buyers don’t ask one question. They ask a run of them as the shortlist narrows. We pick the real versions for your category and ask the same ones every month, so you can see whether anything moved.
Category
“What’s the best project management software for a 30-person agency?”
The broadest question and the most contested. It decides who makes the first shortlist.
Alternatives
“What are the best alternatives to the tool we use now?”
Asked by buyers who are already unhappy with a competitor, which makes them the warmest people in your market.
Head-to-head
“How does one product compare with the other for a small sales team?”
Asked late, when the shortlist is down to two. The engine needs a fair comparison it can quote.
Job to be done
“What tool should I use to automate our month-end invoicing?”
The buyer names the job, not the category. Your product only comes up if your pages describe that job in plain words.
Fit
“Which scheduling software works for clinics in Canada, and does it integrate with our CRM?”
Industry, size, region and integrations. These narrow questions are often the easiest to win first.
What we fix on your site
An engine can only name a product it can read. A lot of SaaS sites are written for someone who already knows the product, so the pages that answer a buyer’s question are missing or locked inside the app.
- 01
Comparison and alternatives pages
Honest pages that compare you with the products buyers already weigh you against. They answer the head-to-head and alternatives questions in words an engine can lift.
- 02
Category and use-case pages
One page per job your product does and per type of team it suits, so a job-to-be-done question has a page of yours to land on.
- 03
Integration pages
A real page for each integration that matters, saying what connects and what it does. “Does it work with our CRM” is a question buyers ask AI directly.
- 04
A pricing page a machine can read
Plans, what each includes and who each is for, as text. If your pricing is behind a sales call, the page can still say how pricing works.
- 05
Docs and help centre crawlability
Help content often lives in an app or a separate tool that crawlers can’t read. We make sure the answers in it can be fetched and quoted.
- 06
SoftwareApplication schema, llms.txt and crawler access
Structured data that says what the product is, what category it sits in and who makes it, a plain-text llms.txt summary, and a robots.txt that lets the AI crawlers in.
What happens off your site
The sources AI cites
When an engine answers a software question it leans on other people’s pages: G2, Capterra, GetApp, TrustRadius and Product Hunt profiles and their reviews, the roundup articles that already cover your category, and Reddit and community threads. We record which of these the engines cite for your questions, then work on the gaps in your profiles and reviews and the roundups that leave you out. On Reddit and in communities we watch and report. We never post fake reviews or plant comments.
One description, everywhere
The engines need to be sure that the product on your site, your LinkedIn page, your Crunchbase record, your review profiles and the press are all the same thing. We set one product name, one category and one line describing what it does, and make every one of those places say it the same way.
The same questions, every month
Every month we ask the same tracked questions in ChatGPT, Gemini, Perplexity, Claude and Google AI. You get a report showing where you were named, which competitors were named, and your share of the answers compared with last month. Same questions each time, so a change in the report is a change in the answers.
What we build ourselves
We do the work
We write and build the comparison, alternatives, category, integration and pricing pages and make the site fixes. You don’t get a list of advice for your web or content team to fit in between everything else.
You see a live scan before you buy
On the first call we ask the engines your buyers’ questions in front of you and show you who gets named. No pitch deck.
We report share of voice openly
Same tracked questions every time. You see who was named in each answer, so you can tell whether it moved.
We’re Canadian
Geongyn is based in Ontario and works with SaaS teams across Canada and the US.
Questions SaaS teams ask
How do I get my SaaS product recommended by ChatGPT?
Give it pages it can read and sources that back them up. Publish comparison, alternatives, category, integration and pricing pages as plain text, with SoftwareApplication and FAQ schema. Describe your product the same way on your site, LinkedIn and every review profile. Then work on the review sites, roundup articles and community threads the engines already cite for your category. There is no form to submit and no shortcut around the sources.
Why does AI name our competitor instead of us?
Usually because the competitor gave it more to read. They may have an alternatives page, a comparison page, more reviews on the sites the engine cites, or a clearer one-line description that matches across the web. Your detail may sit inside the app, a gated PDF or a pricing page that only says contact sales. We run the scan first so you can see which of those it is for your category.
Do G2 and Capterra reviews matter for AI recommendations?
For a lot of software categories, yes. When an engine answers a software question it often leans on review sites and roundup articles to decide who to name. Which ones matter differs by category, so we check which sources the engines actually cite for yours, then work on the profiles and reviews there. We never post fake reviews or plant comments on Reddit or anywhere else.
What is llms.txt and do we need one?
It is a plain-text file at the root of your site that summarises what you do for AI tools. It is quick to add and we include it as standard. It will not get you named on its own. The comparison pages, the cited sources and a consistent description do that work. Treat llms.txt as housekeeping that makes the rest easier to read.
Which AI assistants do you check?
ChatGPT, Gemini, Perplexity, Claude and Google AI, each one separately. They read different sources, so being named by one says little about the others. For every tracked question we record whether you are named, where you appear in the answer, which competitors are named with or instead of you, and which sources the engine cited.
How long before AI names our product?
It depends on where you start and on the engine. Engines that search the web live can pick up a new, well-structured page within weeks. Engines that lean more on training data move slower. Narrow fit and integration questions usually move first. Broad category questions take longest because every product in the category wants them. We re-scan the same questions every month so you can see what moved.
Do you write the pages or just tell us what to do?
We write and build them. Comparison, alternatives, category, integration and pricing pages, the schema and the llms.txt are work we do ourselves, on your site or handed to your team ready to publish. You review and approve everything before it goes live. If your current site can’t carry the fixes, we also design and build websites.
Do you work with SaaS companies outside Canada?
Yes. Geongyn is based in Ontario and works remotely with B2B SaaS companies across Canada and the United States. Being Canadian helps with Canadian SaaS teams, but the tracked questions, the scans and the fixes are the same wherever your buyers are. You see the live scan of your category on the first call.
If you make building products rather than software, the same work for your buyers is set out in GEO for building-product manufacturers.
Thirty minutes. We run your category live on the call.
No pitch deck. We ask the engines what your buyers ask, in front of you, and show you which competitors get named. You keep the findings either way.