Automate Internal Linking at Scale using AI

Internal linking is a vital SEO practice that helps distribute page authority across your website, improves user navigation, and enhances discoverability. However, manually identifying the best spots for contextual internal links can be time-consuming and prone to oversight. This app addresses that challenge by automating the process of identifying natural linking opportunities within a target blog post.

Building the "Add Internal Links" App

Setting up the Inputs

The app starts with three text inputs. The first input accepts a Sitemap URL, which serves as the source for all your blog URLs. The second input takes a Blogs Prefix, allowing you to filter the sitemap and focus only on your blog posts. The third input is for the URL or copy of the target blog post that you want to enhance with internal links.

Step 1: Gathering the Content

In the first step, two functions work together. The Extract Sitemap Urls function retrieves a list of URLs from your provided sitemap, limiting the fetched results to a maximum of 300 and filtering by the Blogs Prefix. This ensures that only your blog posts are considered. Simultaneously, the Scrape Webpage function pulls the main body text from your target blog post. This content forms the basis for identifying opportunities where internal links can be seamlessly integrated.

Step 2: Identifying Relevant Pages

The next function, Semantic Retrieval, compares the content of your target blog post with the list of blog posts extracted from the sitemap. By using semantic analysis, it selects the top 30 most contextually similar blog posts. This creates a curated list of potential internal linking candidates that closely align with the topics discussed in your target post.

Step 3: Injecting Internal Links

In the final step, the Chat Model function, configured with a specialized prompt, takes center stage. It is provided with both the list of relevant blog posts (each with its URL, title, and description) and the content of the target blog post. Acting as an SEO consultant, the model scans the text, identifies natural linking opportunities, and injects the relevant internal links directly into the content. The prompt guides the model to maintain the original tone, adjust wording where necessary, and ensure the links blend in naturally without appearing forced.

Customizing the App for Your Needs

• Adjust the Query Scope: You can modify the semantic retrieval parameters to either narrow or broaden the pool of relevant blog posts based on your content strategy.

• Refine the Chat Model Instructions: Customize the prompt details to better fit the writing style of your brand or to focus on specific sections of the content where links would be most beneficial.

• Tailor the Filters: Change the segment filter in the sitemap extraction step to target different sections of your website, ensuring that the internal linking opportunities are relevant to varying topics.

• Integrate Additional Data Sources: Consider enhancing the retrieval process by incorporating data from competitor blogs or related articles to expand your internal linking network.

Running the App at Scale

1. Prepare a CSV: Create a CSV file containing multiple sitemap URLs, blog prefixes, and target blog post URLs.

2. Mapping Columns: Use the Bulk Runs feature to map each column of your CSV to the corresponding input fields in the app.

3. Execute Bulk Runs: Start the bulk job to automatically process each row, injecting internal links into all of your target posts swiftly and efficiently.

Call to Action

Enhance your SEO strategy by automating internal linking with Moonlit Platform. Sign up today and start building your own apps to streamline and optimize your content workflows.