Quiz And Survey Master ships with rich features: result pages by score range, contact forms, certificates, advanced timer rules, and per-category scoring. The data lives in three core tables, wp_mlw_quizzes for quiz configuration, wp_mlw_questions for the question bank with answer arrays and point values, and wp_mlw_results for every completed attempt with the serialized answers, score, and result type. The default result page renders a static block based on score range. After that, the conversation ends.

SleekAI plugs into those tables directly. The bot reads the latest wp_mlw_results row for the visitor's session or user_id, joins to wp_mlw_questions for the correct options and category tags, and can reference the exact configuration from wp_mlw_quizzes including pass mark, result type cutoffs, and contact form behaviour. A display condition scoped to the QSM result page slug means the bot only appears where it has data to draw on.

Generic chatbots cannot reach any of this. They see only the page HTML, which tells them the score and result type but not which questions belong to which category, what the user picked, or what the correct answer was. They cannot compare this attempt to a previous one. SleekAI references concrete answer choices, category breakdowns, and prior attempts, and stays inside whatever guard rails the system instruction enforces.

A QSM result page is a single moment of high intent. The visitor just spent two to ten minutes answering questions and is staring at their score. That is the best window to upsell, cross-sell, qualify, or coach, but the static result type block leaves the conversation half finished.

They might know they got Intermediate, but they do not know what stopped them from Advanced, which category they were weakest in, or whether retaking is even allowed. A static result page cannot answer follow-ups. SleekAI turns that page into an interactive review session.

It reads wp_mlw_results, joins to the question and category metadata, and lets the visitor probe their own performance. For lead-generation quizzes the same approach works in reverse: the bot can interpret a typed personality result, reference the contact fields the visitor just submitted, and recommend the next page or product. Every chat is logged with quiz_id and result_id, so marketers see exactly which result types lead to longer conversations and higher engagement.

The default page gives a snapshot. The bot gives a debrief. Over hundreds of attempts the debrief is what builds trust and pulls the next click.