The Notion AI SEO System I Use to Run My 100K Visits/mo Business

Landmark Labs credits its SEO growth—rising from zero to about 100,000 views per month—to one operational habit: publishing consistently. The bottleneck isn’t SEO knowledge; it’s the friction of starting from a blank page. The system built in Notion is designed to remove that friction by turning keyword research, drafting, repurposing, and performance tracking into a repeatable workflow.

The workflow starts with a Notion “keywords” database that stores each keyword alongside estimated search volume and difficulty. A built-in scoring rule flags “low difficulty + enough volume” targets as quick wins (marked with an Apple emoji). To expand coverage without doing everything manually, Notion AI is used to generate related long-tail keywords from a main topic. For example, a broad term like “notion AI” can be expanded into more specific angles such as “notion AI for project management” or “notion AI for productivity,” which are more realistic ranking targets.

To validate and refine opportunities, the process loops between Notion and an external keyword data tool called Keywords Everywhere. Searching a phrase like “how to use notion AI” pulls in volume and competitiveness figures, which then feed back into the Notion keyword record. The workflow supports rapid iteration: clicking update can generate a fresh batch of keyword ideas, letting the user pivot quickly toward topics that balance search demand with lower ranking difficulty.

Once a keyword is chosen, the next step is creating a new “post” entry in a content database using an article template. The template prompts for key inputs—article title, target keywords, and a content brief—so drafting doesn’t begin from scratch. Notion AI can also generate multiple SEO-friendly article titles via a custom AI autofill property. The content brief is treated as a critical control document: it can include instructions for length and structure, the target keyword, secondary keywords, and—importantly—reference material for up-to-date facts. The creator recommends pasting in relevant documentation (including Notion’s own guides on Notion AI features like writer and autofill) so the draft is grounded in current product details.

After the brief is complete, Notion AI produces a full article draft. The system then shifts from creation to distribution. A “repurposing” section pre-fills platform-specific instructions (for example, LinkedIn posts, tweets, Instagram captions, and YouTube descriptions). Each repurposed post is generated from the article content and tailored to the platform’s style, then scheduled for publication. Finally, Notion charts track performance by month and by channel, updating view and engagement metrics as posts accumulate results.

The core takeaway is that SEO consistency becomes achievable when the workflow is pre-built: keyword discovery feeds drafting, drafting feeds repurposing, and repurposing feeds measurable outcomes. The result is a pipeline that makes it easier to publish regularly—an outcome presented as the biggest driver of sustained SEO growth.