AI Blog Archive Optimization

I run a sizeable archive of industry-news articles and I’m ready to turn it into a stronger traffic engine. The single, clear objective is to increase visits and session depth by serving intelligent content recommendations to every reader who lands on an article, category page, or search result.

Here’s what I need:

• Analyse the existing archive (CMS is WordPress) to understand topics, dates, and engagement signals.
• Build or configure an AI recommendation layer that suggests related news pieces in real time. Open-source libraries, a SaaS API, or a lightweight in-house model are all options as long as the end result is fast, relevant, and easy for me to tweak.
• Inject the recommendations into the site—widget, sidebar, or inline block—without hurting load speed.
• Provide a dashboard or at least a clear report that shows click-through rates, top suggested articles, and overall uplift so I can measure the traffic gains.

Acceptance criteria: a working recommendation block visible on production, average CTR on suggested links above 4 % after two weeks, and a short hand-off guide so I can maintain or expand the system later (for example, adding automated summaries or sentiment analysis down the road).

If you have previous experience boosting news or blog traffic with AI tools, please share a quick example when you reply.