CASE STUDY
Enterprise SaaS Tackles Competitive AI Visibility Gaps Through 989 Off-Site Assets
Summary:
An enterprise software company lagged a leading competitor in AI-generated answers. Citation Labs expanded the client's off-site visibility through a targeted campaign, with the client mentioned in 87% of answers citing campaign placements versus 57% of answers to the same prompts without those citations.
Results Snapshot
- 374 of 989 assets cited by AI assistants
- 61 of 62 use cases with cited placements
- 87% vs. 57% brand mentions with vs. without asset citations
- 52 of 55 use cases had higher brand inclusion when assets were cited
- 18-percentage-point median difference in brand mention rates
The Challenge: A Leading Competitor Dominated AI Visibility
A large enterprise software company faced significant gaps in how often AI assistants mentioned its products. Meanwhile, a leading competitor appeared more frequently in more than 80% of the use cases the company monitored.
While the client had established visibility in some areas, that strength didn’t extend across its full portfolio.
When people asked AI assistants which tools could help them accomplish specific tasks, AI-generated answers often gave its competitor greater visibility even though the client offered relevant capabilities.
The company couldn’t rely on its established brand presence to ensure it appeared in answers across all its product categories.
Instead, they needed to strengthen their presence in these AI-generated answers, particularly where competitors dominated, and understand whether their visibility efforts were making a measurable difference.
Our Solution: Targeting 3rd Party Sources AI Assistants Cite
Citation Labs developed an off-site AI visibility campaign targeting the third-party sources AI assistants use to answer questions about the client’s products.
We focused on getting the client’s offerings into the information AI assistants already cited or searched for, rather than relying solely on the client’s website.
We used three complementary approaches:
- Adding brand mentions and links to existing third-party pages that AI assistants already cited.
- Creating and publishing articles addressing the follow-up searches AI assistants made when researching relevant questions.
- Building product review and comparison sites that included the client’s offerings alongside competing products.
Each approach addressed a different way AI assistants find and cite information.
Together, they gave the client multiple opportunities to appear in the third-party sources informing AI-generated answers across its product portfolio.
Our Process: Deploying and Measuring Off-Site Campaigns Across 62 Use Cases
The campaign covered 62 product use cases between September 2025 and October 2026. Citation Labs combined off-site asset development and placement with AI visibility analysis and measurement across five workstreams:
- Analyzed AI visibility and citation patterns. Used Xofu to examine competitor mentions, citation share, cited page types, and query fan-outs across monitored prompts.
- Secured 342 link insertions across 185 publisher sites. Targeted pages AI assistants cited, documenting what each page said before and after the edit.
- Published 552 query fan-out articles across 527 sites. Created content to address specific subquestions AI assistants searched when researching relevant use cases.
- Built 95 product review sites containing 1,866 pages. Organized comparison content around the client’s product use cases and competing offerings.
- Tracked citation and brand mention behavior. Analyzed 674,820 AI answers across five AI surfaces, comparing brand mention rates in answers citing campaign assets with answers to the same prompts that didn’t cite those assets.
The Roadblocks: Scaling Delivery Within a 90-Day Window
Working within a 90-day campaign cycle, we needed to launch placements and show impact before the quarter ended. We added team members, assigned daily production tasks, and frontloaded the work into the first 45 days. This left time to monitor citations and brand mentions before the quarter ended, so we could adjust our strategy as needed.
The campaign also required managing three different tactics within the client’s existing budget. Link insertions, query fan-out articles, and product review sites each required different levels of effort. We developed a units-of-work approach to plan and budget each tactic separately, allocate team resources, and measure the impact on AI visibility.
The Toolstack
The platform tracked brand mentions, citations, and query fan-outs across five AI search surfaces, allowing Citation Labs to evaluate how its placements appeared in AI-generated answers.
The Results: Higher Brand Inclusion When Campaign Sources Were Cited
AI assistants cited 374 of the campaign’s 989 assets across 61 of 62 monitored product use cases. Those sources appeared in 17,709 answers to the tracked prompts.
When answers cited campaign assets, they mentioned the client 87% of the time, compared with 57% of answers to the same prompts that didn’t cite those sources.
The pattern held across most of the portfolio: 52 of 55 use cases with sufficient data showed higher brand mention rates when campaign sources appeared, with a median difference of 18 percentage points.
Results varied by tactic. AI assistants cited 73% of product review sites, 57% of link insertions, and 20% of query fan-out articles. When cited, those sources appeared in answers mentioning the client 90%, 80%, and 95% of the time, respectively.
The findings show that campaign assets became cited sources in AI-generated answers and were consistently associated with higher brand inclusion, though they don’t establish a causal lift.
Key Results
- 374 of 989 assets appeared as citations in tracked AI answers.
- 61 of 62 use cases had at least one campaign asset cited.
- 87% vs. 57% brand mentions in answers citing campaign assets versus those that didn’t.
- 52 of 55 use cases had higher brand mention rates when campaign assets were cited.
- 18-percentage-point median difference in brand mention rates with versus without asset citations.
What’s Next?
The campaign’s results led the client to expand its investment in AI visibility through additional work.
We’re using ongoing citation and brand mention tracking to adjust subsequent placements, prioritize opportunities, and use the client’s budget more effectively.
Client Overview
The client is an enterprise software company offering a broad portfolio of digital products and services to individuals and businesses. It operates across multiple competitive software markets.
- Industry: Enterprise SaaS
- Size: 10,000+ Employees
- Location: United States