How a B2B SaaS Company Won Real AI Search Visibility

B2B SaaS AI Search Visibility: Overview

The client is a B2B SaaS / OTT streaming platform. Prior to this work, the site had a growing organic footprint but effectively no presence in AI-generated answers — the layer increasingly sitting between a search query and an actual click. This case study covers the SEO and AI search visibility work carried out over roughly a two-year window, tracked through to September 2026.

Project Duration

Growth shown below is tracked over a 2-year window (2024–2026), based on the full historical range available in Semrush’s traffic and keyword data for the domain.

Challenges

• No meaningful visibility in AI-generated answers (ChatGPT, Google AI Overview, AI Mode, Gemini) despite reasonable traditional SEO groundwork already in place
• Content wasn’t structured in a way AI engines could easily lift and cite as a direct answer
• Underlying technical SEO gaps limiting how efficiently the site could be crawled and indexed
• Keyword targeting wasn’t aligned to the query types most likely to trigger AI Overviews and other SERP features

Objectives

• Build genuine, measurable presence across AI search engines — not just traditional blue-link rankings
• Grow organic traffic and keyword footprint through stronger technical and content fundamentals
• Make key content machine-readable and citation-ready for AI crawlers
• Close technical SEO gaps that were limiting crawl efficiency and indexing

Strategy Implemented

The approach focused on four connected areas — fixing the technical foundation first, then making content genuinely citable by AI engines, then targeting the keywords most likely to surface in AI-driven SERP features.

1. Structured Data Implementation

Structured data (schema markup) was implemented across key pages to make content machine-readable — giving AI crawlers a clearer, more structured signal of what each page actually covers, beyond what plain HTML text conveys.

2. Content Restructuring for AI Citability

Existing content was reworked into clearer, answer-first formatting — the kind of direct, well-structured passages that AI Overviews, ChatGPT, and Gemini can lift and cite directly, rather than needing to interpret loosely-structured prose.

3. Technical SEO Fixes

Site-wide technical fixes were made to remove crawl and indexing barriers, improving how efficiently both traditional search engines and AI crawlers could access and process the site’s content.

4. Targeted Keyword Strategy

Keyword targeting was aimed at both traditional organic rankings and AI Overview eligibility — prioritising the query types and topics most likely to surface in AI-driven SERP features rather than plain blue links.

Results

The figures below are pulled directly from Semrush’s AI Search and Organic Research reports for the client’s domain, as of September 17, 2026.

Campaign Performance Snapshot

“Before” traffic and keyword figures are calculated backward from Semrush’s reported % change, not separately pulled data points. Authority Score, referring domains, and AI Overview keyword counts are current-state figures only — no historical baseline was available for these in Semrush.

Organic & AI SEO Impact

• Sitelinks now appear for 76 tracked keywords, People Also Ask for 23
• AI Overview presence across 191 keywords — a meaningful share of the domain’s SERP footprint
• 1.1K distinct pages now cite the client’s content within AI-generated answers
• Backlink profile grew to 3K backlinks from 790 referring domains, supporting the Authority Score of 30

Data & Tracking Methodology

This case study relies entirely on Semrush’s AI Search & Domain Overview and Organic Research tools, checked on September 17, 2026. No client-side analytics (Google Search Console, GA4) were used for this write-up, since this isn’t a DAG-managed account. The client’s domain name has been withheld at their discretion, so the exact figures can’t be independently re-checked from this page alone — but the reporting tool, methodology, and date are disclosed in full, and nothing here is rounded up or estimated.

Why the Approach Worked

The sequencing mattered: fixing technical crawl issues first meant AI crawlers and search engines could actually reach and parse the content properly. Structuring content for direct citability — rather than relying on traditional keyword density — is what let AI engines lift and cite the client’s pages instead of a competitor’s. And keeping keyword targeting tied to AI Overview eligibility, not just traditional rankings, is why growth showed up in both channels rather than just one.

Conclusion

This case study reflects the kind of work Digital Advance Growth is built to deliver — not just traditional SEO, but the technical and content foundation that determines whether a business shows up in the AI-driven layer of search that’s increasingly standing between a query and a customer.