SaaS buying now starts with a paragraph, not a keyword: “we are a 30-person agency, our PM tool collapsed under client work, budget is $30 a seat.” The assistant answers with two or three products. Generative engine optimization for SaaS is the discipline of being one of them.
B2B buyers use AI to draft their vendor shortlists before touching a review site or a demo form. If ChatGPT names your competitors for your best-fit use cases, you are losing deals you never saw. Our research on how B2B buyers shortlist software with AI found the shortlist often forms in a single conversation.
Four signals dominate: review consensus (G2, Capterra, Reddit threads), presence in the comparison and “best X for Y” content assistants cite, crisp positioning (assistants cannot recommend a product they cannot categorize), and documentation quality. Feature depth matters less than descriptive clarity: the model recommends what it can confidently describe.
“Best CRM” is a bloodbath. “Best CRM for a two-person real estate team” is winnable, and it is how buyers actually ask. Map your genuine best-fit segments and publish direct, specific answers for each. Assistants reward specificity because their users do.
When buyers ask “X vs Y,” assistants synthesize from whatever comparisons exist. If the only comparison is your competitor’s, their framing becomes the answer. Honest, detailed comparison pages, including the cases where you are not the right choice, earn quotes and trust simultaneously.
The metric that matters: across your best-fit buyer questions, how often do the four assistants name you? Aethon tracks this continuously, shows who wins instead and why, then ships the reviews strategy, comparisons, and positioning fixes that raise it.
The work of getting your product named and accurately described when buyers ask ChatGPT, Gemini, Claude, and Perplexity which software to use for their situation.
Increasingly, yes. Buyers describe their team, stack, and budget to an assistant and receive a shortlist. Many arrive at vendor sites with the decision already framed by that answer.
Usually stronger review consensus, better presence in comparison content, or clearer positioning. Assistants recommend what the web’s consensus describes confidently, not what a feature matrix proves.
Very. Assistants lean on review aggregators as trust proxies, and Perplexity and Gemini frequently cite them directly in software answers.
We baseline your shortlist rate across the four assistants, diagnose the citation and positioning gaps, then ship the comparison content, review strategy, and fixes that get you named.