This is where it all comes together:
- In Step 1 you revealed patterns and gaps in the current informational supply via Google results and LLM output — the topical clusters you must cover to maximize topical authority.
- In Step 2 you analyzed how your own pages fit the current supply and which topics to focus on to gain authority.
- In Step 3 you mapped current demand — the typical queries and prompts people use — and found the outliers they search for but do not easily find.
Now fuse the outcomes of each stage into the content strategy that takes you to the top of search results and LLM output for the topic.
Any successful LLMO / SEO strategy must also be strong on the technical aspects: meta tags, schemas, header titles, fast-loading pages (a quick win: convert all images to .webp and add caching), canonical URLs and redirects (to avoid duplication), and backlinks. This tutorial focuses on content.
Once you know the topics to cover, the gaps to address, and the keywords to use, build the content structure for the strategy. The most effective approach starts from the topical structure you identified in the knowledge graph. Those topics can be the backbone of an article via the header tags (H1–H4) for a single piece, or define a hub-and-spoke structure where every cluster becomes a pillar page (a hub) and the connected concepts and entities are covered in adjacent articles (spokes). This signals authority in any subject and gives your site a structure that mirrors how search engines and LLMs see your realm.
To do it, use the AI functionality inside InfraNodus / KeywordGraph: combine all the graphs, then ask the AI module to generate an SEO Content Strategy Outline:
Combining the graphs yields a solid structure for hub and spoke pages, which you can then implement with Claude Code or human writers.
You’ve Completed the Workflow
Run it on your own topic: build the graphs, read the clusters and gaps, and publish into the structure that earns topical authority in search and AI.