Semantic SEO Case Study: Azuno
Background
Azuno is a fast-growing SaaS company specializing in AI-driven analytics. Despite an innovative product, the team struggled to gain traction in search results — their content wasn't ranking for relevant queries, and potential customers were missing them entirely.
The Client Challenge
Azuno, a fast-growing SaaS company specializing in AI-driven analytics, struggled to gain organic traction despite an innovative product. Its content lacked the semantic depth for search engines and AI systems to interpret intent, geo-targeting was weak, and potential customers in its core San Francisco market were missing the brand entirely.
Strategic SEO Solution
Designed a semantic SEO framework tailored to Azuno's needs: reorganized content around entities, context, and relationships to match how LLMs process queries; built localized landing pages and optimized the Google Business Profile to target San Francisco's tech community; and structured content to be machine-readable and easily surfaced in AI-powered search experiences.
Tactical Execution Plan
- Reorganized existing content around entities, context, and relationships instead of isolated keywords.
- Built localized landing pages targeting San Francisco's tech community and optimized the Google Business Profile.
- Structured content to be machine-readable and easily surfaced in AI-powered search experiences.
- Aligned the broader content strategy with how large language models interpret and surface information, future-proofing the SEO approach.
The Methodology
- 1Ran a semantic audit of Azuno's content library against how LLMs interpret intent — product pages read as marketing copy, not machine-parseable assertions.
- 2Reorganized content into entity-centered clusters (capabilities, integrations, use cases) so each page answered one unambiguous question an LLM or human would ask.
- 3Built San Francisco–targeted landing pages and optimized the Google Business Profile to capture the local tech market the brand was entirely missing.
- 4Structured every page with clean headings, direct assertions, and JSON-LD so both Google and AI search experiences could extract answers without inference.
- 5Instrumented 4 months of continuous monitoring across Search Console, ranking positions, and AI-citation checks to prove which clusters compounded fastest.
Key Learnings
- Local + semantic is a compound play: geo-targeted pages captured immediate high-intent demand while entity clusters built long-tail authority in parallel.
- AI platforms surface pages that make unambiguous claims with clean structure — Azuno's answer-first content started appearing in AI-generated summaries within months.
- The 4-month window was enough to show signal: +120% organic traffic and top 3 local rankings for "AI analytics San Francisco."
- Future-proofing isn't theoretical — content structured for entity relationships continues ranking as search engines shift from keyword matching to understanding.
“Farrukh showed us how semantic SEO and geo-targeting could work hand-in-hand with the way AI and language models interpret search intent. His strategy didn't just boost our rankings, it made our content future-proof in an AI-first world.”
Margaret Genet
Head of Marketing, Azuno · San Francisco, CA
Proven Results
Deploy this methodology
This case study proves that pairing entity-based semantic structure with local geo-targeting compounds visibility across both traditional and AI-driven search.