GEO for SaaS companies
Get your SaaS recommended when buyers ask AI for the best tool
When a buyer asks an assistant for the best tool in your category, it names about five products and attaches a sentence of positioning to each. That answer is assembled from review platforms, forum threads, roundup articles, documentation and competitor comparison pages. We work on those sources so your product is named, and described correctly.
The moment
Where the shortlist is actually decided
An operations lead has been told to fix a process by the end of the quarter. She has no incumbent vendor, no shortlist, and forty minutes before her next meeting. She does not open a review site. She asks an assistant for the best tools for her exact situation: team size, budget posture, the two integrations she cannot live without.
She gets five products with a sentence each, asks two follow-up questions about pricing and migration, and pastes three names into a message to her manager. That message is the shortlist. Every later step in the process, including the demos, the pricing negotiation and the eventual purchase order, happens inside a set that was fixed in those forty minutes by a document class you may never have contributed to.
Five products, each with a positioning sentence, a pricing posture and a fit note. Follow-ups about migration effort get answered from comparison pages and forum threads. Products absent from that source layer are absent from the shortlist, regardless of the product.
The sources
Where models get their opinion of SaaS products
Review platforms
Category placement, feature checklists and the free-text review body. The written review is the part that gets lifted: what people say they use it for, what they say broke, and which alternative they compared it against.
Reddit and forum threads
Practitioner threads are weighted heavily because they read as independent experience. A three-year-old thread about a bug you fixed in the next release can still be shaping how your product is described today.
Best-X-tools roundups
The listicle layer of your category, written by content marketers and affiliates. It is uneven in quality and enormously influential in retrieval, because it is written in exactly the shape of the question buyers ask.
YouTube transcripts
Tutorials, walkthroughs and comparison videos carry a spoken description of what your product is for. That transcript is text, it is indexed, and it is often more current than your own marketing site.
Your documentation and changelog
Docs are the most factual thing you publish and the most useful to a model: capabilities, limits, integrations, terminology. A changelog is your only machine-readable proof that the product is alive and moving.
Competitor comparison pages
Your competitors' versus and alternatives pages describe you, in their framing, on a page built to be retrieved for the exact query. If you have no equivalent page of your own, their version of you stands unopposed.
What we find
The three failures we see most
01
No comparison or alternatives content of your own
The single most common gap. Buyers ask comparative questions, models answer them from comparative documents, and you have not written one. The objection is usually that comparison pages feel defensive. The result of not writing them is that your competitors and a handful of affiliates define you instead.
02
Inconsistent category self-description
The homepage, the review profile, the pitch deck language in press coverage and the docs each describe a slightly different product. A model reconciling four descriptions produces a vague one, and vague products lose shortlist slots to specific ones. Fixing this is mostly editing, not building.
03
Docs and pricing that models cannot read
Client-side rendered documentation, pricing behind a form, a changelog inside an app shell, key comparisons in images. Passing a Googlebot check is not the same test. When the factual layer is unreadable, your product gets described from third-party inference, and inference is where the wrong feature claims come from.
The work
What we build
- A tracked prompt set per ICP and use case. The real questions each buyer type asks, in their phrasing, kept stable so results stay comparable over time. This is the brief for everything else.
- A comparison-content programme. Your own versus pages, alternatives pages, migration guides and category explainers, written honestly enough to be read as reference material rather than sales copy.
- Third-party corroboration work. Review platform profiles and review programmes, integration and partner directories, roundup placement, and genuine participation in the communities where your category is discussed.
- One category sentence, used everywhere. The same description of what you are, character for character, across your site, your profiles, your press and your docs, so the model has one picture to build rather than four to reconcile.
- Technical crawler access. Server-rendered docs and pricing, an explicit AI crawler policy in robots.txt, structured data, an llms.txt index, and text that is text rather than an image of text.
Measurement
What we look at when we work together
| Looked at | Definition |
|---|---|
| Citation share | How often you are named across the agreed prompt set, per model, as a rate over repeated runs. |
| Competitor comparison | The same rate for your top three competitors, so the number is relative rather than abstract. |
| Description accuracy | How your product is characterised when it appears, with every wrong feature, stale price and mis-stated limit logged. |
| Source list | The pages the models actually cited, which tells you where the next piece of work is. |
| Change log | What we changed and when, so movement in the numbers can be traced to something real. |
This is sampled and probabilistic, not a rank tracker. Ask the same question twice and you can get two answers, so we run each prompt repeatedly and report rates, trends and sample sizes rather than a position. We publish the same measurement on our own properties, including the months it looks bad.
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Questions
What SaaS teams ask us
How do I get my SaaS product recommended by ChatGPT?
Make the model's picture of you coherent and corroborated. Use one category sentence everywhere, publish your own honest comparison and alternatives content, make sure docs and pricing are readable server-side, and earn independent descriptions of you on review platforms, forums and roundups. Then keep asking the same buyer questions of the assistants and fix whatever is still missing.
Should we publish comparison pages against our competitors?
Yes, and honestly, including the cases where the competitor is the better fit. Comparison and alternatives content is the document class assistants reach for when a buyer asks who else does this, so being absent from it means being absent from the answer. Pages that acknowledge a competitor's genuine strengths tend to be cited more than pages that do not, because they read as reference material rather than sales copy.
Do we need to ungate our docs and pricing for AI assistants to read them?
Docs, yes, in almost every case. Pricing, usually. If a retrieval pipeline cannot fetch and parse a page, the information on it plays no part in how a model describes you, which means your pricing gets described from third-party guesses instead. If commercial reasons make full transparency impossible, publish enough structure to be described accurately: model, tiers, what changes between them.
How long before an AI answer about our product changes?
Some things move within weeks: a blocked crawler, an inconsistent category description, docs that were not server-rendered, a missing alternatives page. Third-party corroboration and content maturity take months, because you are waiting on other people's pages and on models refreshing what they have retrieved. We give a range, check the same buyer questions again as we go, and refuse to promise a date.
Ask us what AI says about you and your top three competitors
Tell us your URL and the three competitors you keep losing deals to. Before we talk, we'll put your buyers' real questions to the assistants ourselves and walk you through how often each of you gets named.
Start the conversation