TL;DR: How Claude and Gemini Differ in AI Search for SaaS
We ran identical B2B SaaS prompts through Claude and Gemini to map how AI search systems generate answers and vendor shortlists.
We captured everything our tooling could reach: subqueries, source citations, brand mentions, and response structure. Then we used UI runs under varied conditions to validate and pressure-test the patterns.
This is what we found:
- Claude and Gemini surface broadly similar numbers of vendors, but the specific brands diverge significantly.
- The source pools are different. They seem to be accessing a different internet!
- Claude is precise. It generates an average of 1.4 targeted subqueries and adds a year qualifier. Gemini generates broader queries (avg 3.5) without a year qualifier and pulls more sources per response (14.1 vs 8.5).
- Claude builds buyer’s guides. Gemini builds compiled references. Both serve buyer intent, but through different editorial approaches.
- UI runs confirm the API patterns, but add a layer: Claude’s shortlist shifts meaningfully by geography and session state. Gemini’s shortlist is more stable across geo conditions but shifts more by model tier.
The bottom line: If you want to show up in AI search results, you need to optimize for how models retrieve and construct answers, not just traditional SEO rankings
How We Compared Claude vs Gemini on B2B SaaS Queries
We identified ~200 prompts to simulate various personas and their purchase journey while evaluating Contract Lifecycle Management solutions. The journey starts with the problem, the possible solutions, comparing the vendors and finally verifying the shortlisted candidates.
For this blog, we considered a subset of the prompts. We chose 10 prompts that a B2B SaaS CEO might run to compare and shortlist CLM vendors.
The 10 prompts were run through both Claude and Gemini via API on the same day. Tooling captured: subqueries, source citations, grounding chunks, brand mentions, response length.
For UI validation we used a single prompt:
“Best contract management software for SaaS startups? Compare the top 5 that are scalable and easy to maintain.”
This is not an open-ended query. The user has:
- defined the category
- asked for comparison
- specified constraints
In traditional search, this would lead to a few listicles, a few product pages, and some tab-switching. In the case of AI search, the answer is a shortlist.
This is what the data is showing us:
How Claude and Gemini Expand Queries in AI Search
Both systems generate internal queries before presenting the shortlist. Consider the following prompt:
“Best contract management software for SaaS startups? Compare the top 5 that are scalable and easy to maintain.”
The subqueries that the two systems generated are quite different.
Claude Subqueries
- best contract management software for SaaS startups 2026
- top contract management software scalable easy to use comparison 2026
- PandaDoc vs Ironclad vs SpotDraft vs Juro vs Concord pricing features 2026
Claude introduces vendor names, compares the vendors more often, and also adds a year qualifier. The queries move quickly toward evaluation.
Gemini Subqueries
- best contract management software for SaaS startups scalable easy to maintain
- top contract lifecycle management software for startups
- contract management software for small businesses SaaS
- scalable CLM for startups
- easy to maintain contract management software
Gemini stays within category variations (.... for small businesses). There are no vendor names, and fewer explicit comparison queries.
What Sources Power AI Search Results
The responses draw from different types of sources. It looks as if they are referring to two different internets. Which sources each system retrieves is an important finding. If your content isn’t in the retrieval pool, your brand won’t be in the answer.
The following is a sample spread of sources across the two models for the responses.
| Source domain | Claude | Gemini | Vendor Type |
|---|---|---|---|
| bindlegal.com | 8 / 10 | 5 / 10 | CLM Vendor |
| aline.co | 5 / 10 | 8 / 10 | CLM Vendor |
| pactly.com | 4 / 10 | 8 / 10 | CLM Vendor |
| ironcladapp.com | 8 / 10 | 0 / 10 | CLM Vendor |
| hyperstart.com | 10 / 10 | 4 / 10 | CLM Vendor |
| contractsafe.com | 2 / 10 | 6 / 10 | CLM Vendor |
| g2.com | 2 / 10 | 2 / 10 | Independent SW Reviewer |
| myshortlister.com | 8 / 10 | 1 / 10 | Independent Vendor Directory |
| juro.com | 8 / 10 | 5 / 10 | CLM Vendor |
| research.com | 4 / 10 | 1 / 10 | Independent SW Reviewer |
Both systems draw primarily from vendor-owned sites that publish category comparison content. The difference is that Claude additionally retrieves from independent aggregators and directories:
- myshortlister.com (8/10)
- and research.com (4/10)
These aggregators are largely absent from Gemini's source pool. Gemini stays closer to vendor-published content throughout.
Since Claude uses independent directories that rank and list vendors more neutrally, its shortlists reflect market consensus.
Gemini relies on vendor-authored content. This means its recommendations favor whoever publishes the best guides.
Which SaaS Brands Appear in AI Search Results and Why
The 10 prompts surfaced approximately 50 brands in both Gemini and Claude responses. Ironclad is the biggest gainer of visibility in Claude, while Hubspot gains the largest in Gemini responses.
The following slope chart shows the rank changes across the models.
The Ironclad divergence:Ironclad appears in 9 of 10 Claude responses but only in 5 of 10 Gemini responses. It has the largest gap for any major brand. In Gemini, Ironclad’s narrative is driven by a few sources other than its own site! E.g. A listicle from the competitor zealdocs.com is used by Gemini. While in Claude, it has presence in responses sourced from many more listicles, vendor directories, and even its own website.
The Juro consistency:Juro is the most consistently surfaced brand in both systems (8/10 Claude, 9/10 Gemini). It is carried by the widest spread of sources in the dataset. 15 distinct domains talk about it. No other brand has that breadth. Ironclad is heavily cited but concentrated in bindlegal.com and ironcladapp.com. PandaDoc is broad but drops on certain prompt types.
How Claude and Gemini Structure AI Search Responses
While both systems ultimately deliver a shortlist, how they construct the answer is fundamentally different.
Claude’s responses resemble a buyer’s guide. They are longer, more structured, and explicitly organized around decision-making. Most responses follow a clear pattern: ranked or labeled tools (“Best for X”), followed by detailed breakdowns, tradeoffs, pricing context, and often a final recommendation or stage-based guide.
The writing reduces ambiguity, telling the user not just what exists, but what to choose depending on their situation. This aligns with its query behavior and source pool: once Claude moves into comparison mode, it commits to helping the user decide.
Gemini’s responses, in contrast, resemble a compiled reference. They are more modular and descriptive: listing tools, explaining features, and outlining capabilities without strongly prioritizing or narrowing options.
The response is less opinionated: tools are presented with strengths, integrations, and use cases, followed by general “key considerations.” Rather than restricting the response to recommendation, Gemini preserves optionality and lets the user decide.
This difference shows up consistently across prompts and environments. Claude optimizes for decision clarity, forming responses to guide a purchase.
Gemini optimizes for information completeness, structuring responses to map the landscape. Both satisfy buyer intent, but at different stages: one helps you choose, the other helps you understand.
How to Rank in AI Search: What This Means for SaaS Vendors
The takeaway isn’t just that Claude and Gemini behave differently. AI search visibility depends on how well your content fits each system’s retrieval and reasoning model.
Claude rewards vendors that show up in aggregated, comparison-driven ecosystems, directories, analyst-style content, and pages that position you alongside competitors. That’s because its query patterns move quickly toward evaluation and it leans on sources that already structure the market.
Gemini, on the other hand, rewards vendors that publish strong, self-positioned category content, clear use-case pages, integration narratives, and feature-led comparisons, because it builds its understanding bottom-up from vendor-authored material.
In practice, this means you can’t rely on a single content strategy. If you only invest in your own blog and landing pages, you may perform well in Gemini but miss out on Claude’s shortlist. If you only chase aggregator rankings, you may gain in Claude but remain underrepresented in Gemini.
The brands that consistently show up across both systems are those that span both worlds: they publish high-quality comparison content themselves and are cited, listed, and discussed across independent ecosystems.
AI search is not just ranking pages, it’s constructing answers. And whether your brand makes that answer depends less on traditional SEO position, and more on whether you are present in the model’s version of the internet it chooses to read from.
How to Improve Your Visibility in AI Search Results
If your buyers are already asking these questions in AI systems then your content strategy needs to evolve from ranking on Google to being included in answers.
That means understanding how different models retrieve, structure, and prioritize information, and ensuring your brand shows up across both vendor-driven and aggregator-driven ecosystems.
If you are thinking about how your product surfaces in AI-generated shortlists, or why it doesn’t, reach out to us. We are actively researching this space and working with teams to map, test, and improve their visibility.
Reach out if you want to dig deeper into how your category is being represented and what it takes to show up consistently.
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