OpenAI Chatbot exposes a chat widget that calls an OpenAI-compatible endpoint and stores each session and message in custom tables (typically a sessions table plus a per-message log). The admin shows a session list and the last few conversations. That is enough to spot-check whether the widget works.

It is not enough to answer the questions site owners actually have: how many visitors talked to the bot last month, how many messages per session on average, which model is doing the work after a recent A/B switch and what the daily token spend actually looks like. SleekView Charts reads the same tables the plugin writes and turns them into a small dashboard.

A Number card surfaces sessions this month. A Pie splits messages across user and assistant roles or across configured models. A Bar groups sessions by entry page so editors see which pages drive most chat. An Area trends daily token usage so spikes line up cleanly with feature releases, traffic surges or a misbehaving prompt.

OpenAI Chatbot ships with the operational view it needs: a recent session list and a per-message log. That is sufficient for debugging the integration and spot-checking output, and not sufficient for running the bot as a product surface. A KPI of monthly sessions tells the team whether usage is growing or quietly fading.

A donut of user versus assistant messages exposes whether replies are running long or whether visitors give up after one exchange. A bar of sessions per entry page shows whether the widget is anchored where the questions actually appear. A daily token area chart makes the cost curve a planning input instead of a quarterly invoice surprise.

Same tables, same messages, completely different operating posture.