B2B SaaS · Case Study

3.4x Visibility in AI Engines — How Was It Achieved in 4 Months?

A B2B SaaS company based in Turkey increased its brand mention rate in ChatGPT and Perplexity responses from 19% to 64% in four months. In this analysis, we examine every step of the process executed with SEOYEN modules.

Customer Profile: Who Is This Company?

The company we worked with is a B2B SaaS provider founded in one of Turkey’s leading industrial cities and grown over 11 years into a mid-sized software firm since its launch. Because of name and industry confidentiality, we’ll refer to it as “Company X.”

Company X’s product is vertical business process management software for SMEs in manufacturing, logistics, and retail. Unlike general-purpose ERP solutions, this software goes deep in a specific vertical and offers inventory management, order tracking, field service integration, and real-time reporting modules. Its customer base consists of more than 340 active organizations across Turkey, each with between 200 and 1,500 employees.

The marketing team has three people: one content lead, one digital advertising specialist, and a marketing director leading the team. They already had traditional SEO efforts in place and ranked on the first page of Google for some target keywords, but the entire infrastructure was built around Google. No system had yet been set up to track the new search behaviors introduced by the AI era.

The Core Problem: Being Invisible in AI Engines

In late 2025, Company X’s marketing director noticed the sales team mentioning a new pattern: about one-third of prospects requesting demos were starting conversations by saying, “I checked ChatGPT, and it recommended this.” The vast majority of those prospects had seen not Company X, but two competitor brands recommended as software options.

The director ran a quick test personally by asking industry-specific questions to ChatGPT, Google Gemini, and Perplexity.

• “Software recommendations for manufacturing SMEs” → Company X was not mentioned at all.
• “ERP alternative comparison for a small business” → Two international brands and one local competitor appeared.
• “Which inventory management software is good?” → The industry’s three major players were listed, but Company X was absent.

A company ranking at an average position of 4.2 in Google search results was almost completely invisible in AI answers. The problem had become clear: Google SEO and AI Visibility were entirely different disciplines, and the company had done no work for the latter.

When they contacted SEOYEN, the goal was singular and measurable: significantly increase AI answer visibility across 28 priority queries within 4 months.

The 4-Month Process: What Did We Do Month by Month?

Month 1 — Discovery and Baseline Measurement

The first month was built entirely around measurement and strategy. Using SEOYEN’s AI Visibility Module, 28 different target queries were sent to ChatGPT (GPT-4o), Google Gemini (1.5 Pro), Perplexity AI, and Anthropic Claude. The responses from each engine were reviewed to record whether Company X’s name appeared, in what context competitors stood out, and which sources the AIs cited.

During the same period, competitors’ backlink profiles were mapped using SEOYEN’s Backlink Analysis Module. We found that Competitor A had published a guest post series on two major industry blogs, and those blogs were among the sites ChatGPT frequently cited as sources. Competitor B, meanwhile, was using FAQPage schema far more extensively; the structured data on its product comparison pages was being quoted directly by Gemini.

Baseline table (end of Month 1): Measurements were taken across 28 queries. Company X’s name appeared in the answer of at least one AI engine for only 5 of those queries (18.9%). The average appearance rate of 4 competitor companies was 47%.

Month 2 — Rebuilding the Content Architecture

With SEOYEN’s Keyword Research Module, we identified software comparison phrases that AI engines used with high frequency. These phrases were divided into three categories: (1) direct product comparisons (“X software or Y software?”), (2) use-case-focused queries (“how should manufacturing SMEs choose inventory software?”), and (3) broader industry guides (“how many months does ERP implementation take?”).

Within this framework, 6 new long-form content pieces were created. For content production, the company’s in-house marketing team and a freelance writer with domain expertise were used instead of SEOYEN’s AI features — because AI engines tend to classify human expertise as a more reliable source.

The following schema types were added to each content piece:

• SoftwareApplication: Product name, category, operating system support, price range, and review score were marked up as structured data.
• FAQPage: “Frequently Asked Questions” blocks were added beneath each piece; the questions were selected from real queries asked directly to AI engines.
• HowTo: Step-by-step guide content such as “How should inventory management software be evaluated?” was marked up with HowTo schema.
• Review (aggregate): Existing customer reviews were compiled and integrated into the product page with AggregateRating.

During the same period, guest content pitches were sent to 3 previously identified industry publications. 2 of them accepted, and the articles went live at the beginning of Month 3.

Month 3 — More Citations and Ongoing Monitoring

Month 3 focused on making the content “citable by AI.” Two critical steps were taken for this:

First, Quick Answer Optimization: AI engines often copy or paraphrase specific paragraphs in their answers. Those paragraphs need to be concrete, concise, factual, and focused on a single topic. Company X’s existing content was restructured, and 2-3 sentence “AI-ready summary” paragraphs were added to each section.

Second, E-E-A-T Signals: The “Experience, Expertise, Authoritativeness, and Trustworthiness” (E-E-A-T) signals that influence Google’s, and therefore AI engines’, trust evaluations were strengthened. The authors’ LinkedIn profiles were linked from the content pages, and the company’s founding date and certifications were moved into more visible sections.

Weekly automated scans were set up with the SEOYEN AI Visibility Module. Each of the 28 queries began to be checked across all engines twice a week, and the results were tracked in the SEOYEN dashboard. By the end of Month 3, visibility had increased from 18.9% to 41%.

Month 4 — Growth and Conversion Impact

In Month 4, the focus shifted to reflecting the visibility gains into sales channels. A “recommended solution in AI engines” badge was added to the demo page, along with a real-time updatable summary showing which engines it appeared in. The A/B test ran for 3 weeks; the version with the badge and social proof block increased the demo conversion rate by 22%.

During the same period, 2 additional guest articles were published, and 6 new backlinks were earned (Month 4 total: 8 authority backlinks). A comparison article published on a leading technology portal in the industry became content that ChatGPT cited as a source for 4 different queries within one week.

By the end of Month 4, Company X appeared in at least one AI engine’s answer for 64% of the target queries (18/28), usually in a positive context as a solution “recommended for evaluation.” Demo requests increased by 78% compared with the previous period.

Visibility by AI Engine: Before and After

The table below shows the rate at which Company X’s name appeared in responses from all four engines across 28 target queries. It answers the question of how many of the 28 queries produced a brand mention for each engine.

* All 28 target queries were examined separately for each engine. The values are the Month 4 average of weekly scan results from the SEOYEN AI Visibility Module.

Which Query Types Produced the Fastest Results?

The 28 queries progressed at different speeds. By intervention type, the breakdown looked like this:

Quick Wins (6-8 Weeks)

The fastest progress was observed in query categories where FAQPage schema was added. In direct definition queries, especially formats like “What does X software do?” and “What does Y feature mean?”, Gemini began directly citing the FAQPage schema blocks on the product page. In this category, visibility rose from 14% at the start to 58% within 8 weeks.

Example query types: “how does business process management software work”, “what is inventory tracking software used for”, “what are the advantages of industry-specific software for SMEs”.

Mid-Term Wins (10-14 Weeks)

Comparison and decision-support queries fell into this category. ChatGPT and Perplexity give more weight to reliable third-party sources (blog posts, independent portals) for these kinds of queries. About 3-4 weeks after the guest articles were published, visibility in this category began to increase meaningfully.

Example query types: “ERP comparison for a small business”, “which manufacturing software is suitable”, “industry-specific ERP alternatives in Turkey”.

Longer-Running Queries (16+ Weeks)

Progress was slower for direct comparison queries against specific competitor brands. In these queries, AI engines appeared to require more established, multi-source references. By the end of Month 4, 34% visibility had been achieved in this segment; the process is expected to push that rate even higher in Months 5-6.

How Did SEOYEN Modules Fit into the Operation?

Competitor Comparison: Where Did It Move in 4 Months?

At the start of the project, Company X ranked last in visibility across the tracked 28 queries among 4 competitor companies. While competitors averaged 47% at the start, Company X began at 19% — roughly 2.5 times behind its main competitors.

By the end of Month 4, Company X had surpassed two of those competitors with a 64% brand mention rate, and the gap with the leading competitor, Competitor A (71% vs 64%), had narrowed. Its within-market visibility ranking rose from 4th place to 2nd.

One notable finding: Competitor A’s visibility advantage came entirely from long-term backlink accumulation, and surpassing that buildup in the short term was not realistic. However, by the end of Month 4, Company X had outperformed all competitors in content quality and schema usage. This indicates real potential to take the lead in the mid-term (6-8 months).

“Our customers now ask ChatGPT about software and come to us after choosing one. When I first saw SEOYEN’s AI Visibility report, I realized 80% of that choice was going to our competitors. Four months later, we had turned it in our favor. It wasn’t just visibility; the quality of incoming demos changed too — now people arrive already knowing the product and comparing options.”

4 Critical Lessons from This Case