Your buyers ask AI which software to shortlist. Be the answer.
B2B SaaS buying increasingly starts in ChatGPT, Google AI Overviews, Perplexity, and Gemini long before a demo request. Buyers ask for "the best tool for X," compare categories, and arrive with a shortlist you never influenced. NYFTY Labs works to make your product far more likely to be the one those engines name, cite, and describe correctly, while still winning the classic search, review-site, and paid channels your pipeline depends on. Marketing plus AI systems, built for how software actually gets evaluated.
Software gets shortlisted in AI answers now, not just search results
The B2B SaaS buying committee has moved its research into AI. Before anyone requests a demo, someone has asked ChatGPT to compare your category, checked an AI Overview for "alternatives to" a competitor, or had Perplexity summarize the top tools with citations. If the engines don't know your product exists, describe it wrong, or cite competitors instead of you, you lose the deal before it enters your CRM, and you never see why. Winning now means being the answer the engines give, being accurate on the review platforms they trust, and still ranking and converting in the classic channels. NYFTY Labs builds both halves: the AI-visibility and GEO work that makes you far more likely to be named correctly, and the marketing plus automation systems that turn that visibility into trials, demos, and pipeline.
Outcomes, not activity.
Your product is far more likely to be named and described accurately when buyers ask AI engines for the best tools, alternatives, and comparisons in your category.
Coverage for the comparison, 'vs', and 'best tool for' queries where SaaS shortlists are actually decided, not just top-of-funnel blog terms.
A coordinated presence across the review and citation sources (G2, Capterra, docs, trusted publications) that AI engines pull from when they answer.
Trial-to-paid and demo-request paths tightened with conversion work, so more of the qualified traffic you earn becomes pipeline.
Lead routing, enrichment, and lifecycle steps automated with AI so marketing scales output without adding headcount.
Clear measurement tying channels, pages, and AI citations to qualified pipeline instead of guessing which effort worked.
Where B2B SaaS loses the lead now.
- An AI answer recommends three competitors by name for your category and never mentions you, so buyers arrive with a shortlist you were never on.
- Engines describe your product with an outdated feature set, the wrong ICP, or a category you exited two pivots ago, and buyers believe it.
- Your content ranks for blog keywords but not for the comparison, alternatives, and 'best tool for' queries where deals actually get shaped.
- Review platforms like G2 and Capterra shape the AI's answer more than your own site does, and you have no coordinated presence there.
- Trial and demo signups fluctuate and you can't tell which channel, page, or AI citation is actually driving qualified pipeline.
We turn those gaps into booked demand.
Get a planWhat is B2B SaaS marketing in the AI era?
B2B SaaS marketing is the work of getting a software product found, understood, and shortlisted by the businesses that buy it, across AI answer engines, organic search, review platforms, and paid media, then converting that attention into trials, demos, and pipeline. For NYFTY Labs it means two things at once: increasing the chances the AI engines buyers now consult describe your product accurately, and running the growth-marketing and AI-automation systems that turn qualified interest into revenue.
How it works
It starts by auditing how ChatGPT, Google AI Overviews, Perplexity, and Gemini currently answer the category, comparison, and "best tool for" questions your buyers ask, then mapping where you are missing, mis-described, or out-cited by competitors. From there we build the entity, content, and third-party signals, clear product and use-case pages, comparison and alternatives content, structured data, and citations from the review sites and sources these engines trust, so it's easier for the models to name and describe you correctly. In parallel we run the growth stack that still moves pipeline: SEO for high-intent queries, paid media where it pays back, conversion work on trial and demo paths, and AI automation for lead routing, enrichment, and lifecycle so marketing output scales without adding headcount.
Who it’s for
It fits B2B SaaS companies whose buyers self-educate before ever talking to sales, from seed-stage startups defining a category to enterprise platforms defending one. The outcome that matters is not vanity traffic but qualified pipeline: being present and accurately described in the AI answers and searches where shortlists form, more of the right trials and demos, and a marketing engine you can measure and trust. It is a fit whether you sell product-led with a free trial or sales-led with a longer enterprise cycle.
In practice
A workflow-automation SaaS finds that when buyers ask ChatGPT and Perplexity for "the best tools" in its category, two competitors are named every time and it is absent, and Google's AI Overview still describes an old positioning from before its last pivot. NYFTY audits the gap, rebuilds the product and use-case pages with clear structured data, publishes honest comparison and alternatives content, and earns citations from the review sites and sources those engines pull from. Over the following quarters the engines grow far more likely to name the product accurately in category answers, and demo requests arrive from buyers who already understand what it does.
The stack for your vertical.
Go deeper by specialty.
Questions, answered.
SEO and content are still part of it, but they target ranked links for a human to click. AI engines answer the question directly and often name a specific shortlist without a click at all. That requires different work: structured, quotable pages the models can lift from, accurate entity and category signals, comparison and alternatives content, and citations on the third-party sources these engines trust. We run both, classic search for the queries that still send clicks, and GEO/AEO so you're present in the answers that don't.
No one can dictate what a model outputs, and we won't claim otherwise. What we can do is change the inputs those models draw from, your own pages, your structured data, the review platforms, and the third-party sources they cite, so the accurate picture is the one that's easiest for them to find and repeat. When the source material is clear, consistent, and well-cited, the engines describe you more accurately more often. We measure it over time rather than promising a specific output.
It depends on your motion. If you're still finding product-market fit, heavy marketing spend is usually premature. But the entity and category-visibility groundwork, clear positioning pages, accurate structured data, getting the engines to describe you correctly, is cheaper to build early than to correct after the engines have already learned the wrong story. We'll tell you honestly which pieces make sense now and which should wait until you have a repeatable motion to pour fuel on.