AI Power calls model vendors and then drops the response back into WordPress as a post, an image attachment or a chatbot log entry. The conversation lives in the vendor's logs. The artifact lives in WordPress: a row in wp_posts, a postmeta block tagging the model, and (for chatbot use) rows in AI Power's own logging tables.

That artifact is what an editorial or chatbot owner can govern. SleekView Charts reads those rows directly. A Number card counts generations. A Pie splits by model. A Bar groups by author or by chatbot. An Area trends generations per day. The dashboard is built from real database rows, not from a probe call to the model vendor.

The chart view and the table view share the same dataset, so filters carry across. Filter to the last 30 days in the chart view and the audit table narrows the same way. Exports come straight from the filtered rows. Cloud generations stay with the vendor; SleekView surfaces what is already stored in WP.

AI Power covers writing, images and chatbots in one plugin, and that breadth means the underlying volume sneaks up on a team fast. Counting log rows or scrolling the Posts screen does not change behaviour. Watching total generations as a KPI, model share as a pie, per-author volume as a bar and daily cadence as an area moves the conversation from impressions to numbers.

The same data, surfaced as chart cards, becomes the brief for a quarterly review, a cost optimisation or a decision to swap a model in the defaults.