Selecting a specialised GEO and AI search agency for B2B SaaS comes down to three checks: does the agency have a methodology built for how large language models retrieve and cite content, rather than a relabelled SEO service; can they measure AI visibility with metrics that don’t exist in traditional analytics; and can they show B2B SaaS experience specifically, given how different software buying committees are from consumer search behaviour.
In our experience, many agencies now claim GEO capability and far fewer can demonstrate it. This guide sets out what separates the two, and what to verify before signing a contract.
Key takeaways
- A genuine GEO specialist prioritises visibility in ChatGPT, Gemini, Claude, and Perplexity alongside traditional rankings, not as an afterthought bolted onto an existing SEO retainer
- Ask for named examples of B2B SaaS brands the agency has helped get cited by generative engines, not general AI search commentary
- AI visibility measurement requires different metrics to organic search: mention rate, citation rate, and share of voice within AI-generated responses, tracked per platform
- Content that earns AI citations is structured differently to content that ranks well: in our experience, answer-first passages, comparison formats, and integration pages earn the most citations
- This piece focuses on identifying genuine specialism and measurement rigour; for a full evaluation framework, see our agency evaluation scorecard guide
Why many agencies claim GEO capability but few demonstrate it
Software marketing teams are now fielding pitches from agencies that have added “GEO” to their service list, in our experience often without changing much else about how they work. Distinguishing a genuine specialist from a rebrand is the first and most consequential filter in the selection process, and it’s worth spending real time on before comparing pricing or timelines.
Evaluating agency expertise in AI search and GEO
The clearest signal of genuine GEO expertise is whether an agency built its methodology around how generative engines actually retrieve and synthesise content, or whether it took an existing SEO service and renamed the deliverables. Legacy SEO agencies optimise for ranking position and organic click-through. AI search optimisation optimises for whether a brand gets surfaced, cited, and accurately described inside an AI-generated answer, which relies on different inputs, as we explain in how AI search engines rank and retrieve websites.
Ask direct questions. Does the agency track visibility separately across ChatGPT, Gemini, Claude, and Perplexity, given that these platforms pull from different sources and behave inconsistently with one another? Can they explain, in specific terms, how large language models select and weight source content when constructing an answer (see how ChatGPT decides which brands to recommend)? Do they have a defined process for prompt mapping, building out the actual questions your buyers ask at each stage of the decision, rather than treating AI search as a variation on keyword research?
Request named B2B SaaS examples, ideally with some detail on what changed and over what timeframe. Our own view on why this is a genuinely difficult specialism to claim, given how fast the underlying models change, is covered in our piece on whether AI search expertise is possible to sustain. An agency that acknowledges the field is still forming, while showing a rigorous and evidence-based process, is a more credible partner than one claiming certainty it can’t back up.
Measuring performance in generative engines: which KPIs matter
Traffic and keyword rankings, the default KPIs for a traditional SEO retainer, don’t capture what’s happening in AI search. A brand can be cited in AI-generated answers without producing a trackable referral session, because users often read a ChatGPT or Perplexity response without clicking through. Judging a GEO agency by organic traffic alone will miss most of the value they’re meant to be delivering.
The metrics that matter instead are AI-specific: mention rate (how often your brand appears across a defined set of buyer-relevant prompts), citation rate (how often that appearance includes a direct citation or link), and share of voice within AI-generated responses relative to named competitors. Each of these needs to be tracked per platform, since a strong presence in Perplexity says very little about performance in Google AI Overviews or ChatGPT, which draw on different indexes and weight sources differently.
A specialist agency should be able to describe, in concrete terms, how they capture this data on an ongoing basis, how frequently they re-run the prompt set, and how they separate AI-referral traffic from generic organic and direct traffic in your existing analytics stack. We’ve written a more detailed breakdown of the specific KPIs worth tracking, and how to set realistic targets against them, in our guide to the metrics that actually matter for a GEO campaign.
Optimising content for AI visibility
The content structures that earn AI citations are not the same ones that rank well in traditional search, and an agency’s approach to content should reflect that distinction directly. In our experience, generative engines tend to retrieve and synthesise information at the passage level, extracting specific sections of a page rather than evaluating the page as a whole. That changes what “good” content looks like.
Answer-first passages, where the direct response to a likely query sits near the top of a section rather than buried under narrative build-up, tend to be easier for AI systems to lift and cite accurately. Structured comparison content, laid out clearly against named alternatives or approaches, performs well because it gives a model an extractable, low-ambiguity answer to a comparison query. Integration pages, which describe specific technical compatibility in concrete terms, tend to be cited because they answer a narrow, specific question unambiguously.
An agency worth hiring should be able to walk through how they restructure existing content for this kind of extractability, not just how they produce new content. That includes formatting decisions like section length, heading structure, and where the direct answer sits relative to supporting context, all of which affect how confidently a generative engine can lift a passage and attribute it correctly.
A short evaluation checklist
Before signing with any agency, ask them to answer the following directly:
- Which AI platforms do you track visibility on, and how do you measure it differently across each one?
- Can you name specific B2B SaaS clients and describe what changed in their AI citation footprint?
- How do you build and maintain a prompt set that reflects our actual buyer journey, not a generic industry list?
- What does your content restructuring process look like for pages that already rank but aren’t being cited by AI systems?
- How do you report AI referral traffic separately from organic and direct traffic in our existing analytics setup?
- How many times do you run each prompt, and is the prompt set fixed so that changes over time mean something?
- How do you identify the recurring sources that influence the prompts that matter commercially?
- Once you have found an opportunity, what work do you actually carry out?
An agency that answers all eight with specifics, rather than general reassurance, has likely built the capability the market increasingly requires. For a broader roundup of who else is working in this space and how they position themselves, our review of leading AI search optimisation agencies is worth reading alongside this checklist.
For the wider vetting process, including what GEO reporting should include, see our guide to evaluating a GEO agency for B2B SaaS.
Choosing the right partner here isn’t a matter of picking whoever pitches the most confidently. It’s a matter of verifying a specific, demonstrable methodology against the way generative engines actually work, and holding any agency, including one that specialises exclusively in this space, to evidence rather than claims.