LLM-Driven SEO Strategy That Delivered 3.5x Organic Growth for an Airline Website

To address these challenges and achieve the objective, a comprehensive LLM-optimized SEO strategy was implemented. This strategy combined content enrichment, structured data, technical hygiene, and semantic organization to align both traditional SEO and generative search needs.

1. LLM-Ready Content Clusters (Route Pages)

We revitalized core route pages by blending high-utility travel data (flight schedules, timetables) with human-written narratives about destinations. This created the “semantic depth” LLMs use to determine topical authority. Human nuance satisfies quality content guidelines, while structured factual data offers grounding for AI systems.

Benefits: Increased engagement, enhanced topical relevance, and stronger citations in AI outputs.

2. Conversational Q&A Integration

We introduced structured FAQs that reflect natural language user queries tied to flight planning (e.g., baggage rules, layovers). Modular FAQ blocks were designed so that LLMs could easily extract and surface answers.

Benefits: Higher likelihood of appearing in AI “People Also Ask” features and AI-generated summaries.

3. Semantic Schema & Structured Markup

Specialized schema markup — including FAQ, How-to, and flight-specific structured data — was deployed across the website. Schema makes content machine-readable, improving the context and interpretation of pages by both search engines and LLMs.

Benefits: Richer search snippets and more efficient crawling.

4. Keyword Expansion for AI & Semantic Relevance

We systematically launched new route-specific pages to capture long-tail, high-intent queries that were previously unaddressed.

Benefits: Expanded visibility footprint and opportunities to capture early-stage search intent.

5. Internal Linking Architecture

Sophisticated internal linking was implemented to connect related content (for example, linking travel guides to specific route pages). This created a logical content network that guides both users and crawlers.

Benefits: Enhanced crawl depth and stronger contextual relationships across topics.

6. Technical Hygiene (404s & Redirects)

A full audit corrected broken links and removed redirect chains. Cleaner technical structure ensured that crawler budget was spent on active, relevant pages.

Benefits: Smoother crawling and improved reliability signals to search engines.

7. Intent-Aligned Meta Optimization

Meta titles and descriptions were rewritten to be more descriptive and click-oriented. While LLMs use body content heavily, meta tags still influence ranking click-through signals and can act as concise summaries for automated tools.

Benefits: Higher SERP click-through rates and clearer page intent signaling.

8. Quora And Reddit Conversational Mentions 

We monitored and engaged in relevant discussions on platforms like Quora and Reddit to understand real user questions and conversational language that LLMs increasingly rely on when generating answers. By analyzing frequently asked questions and common phrasing from these communities, we informed our content creation, expanded our FAQ topics, and ensured our site reflected authentic user intent

Benefits: Quora and Reddit are rich sources of genuine user queries and conversational patterns that large language models often reference when answering natural language questions. Participation and insight-gathering from these platforms helped us uncover real-world search intent and long-tail phrasing that improved our content’s alignment with LLM retrieval logic

9. AI Overview Optimization with Infigrowth

To measure and monitor our performance specifically in AI Overview (AIO) visibility, we used our in-house analytics platform, Infigrowth. Infigrowth is an AI-powered SEO intelligence suite that helps digital teams track keyword rankings, search trends, and organic visibility — including how frequently our content appears in AI-generated overviews and LLM-driven response surfaces.

With Infigrowth, we were able to:

• Track changes in AI Overview keyword visibility over time and quantify improvements in AIO presence.
• Monitor LLM traffic insights and AI visibility scores that show where our content is being recognized by large language models. 

Analyze keyword performance, search trends, and large-language-model impact metrics in a unified dashboard. This helped us refine our content strategy and focus on topics most likely to be referenced by conversational systems.