Yes. SleekAI reads your courses as WordPress custom posts and pulls in custom fields, so any LMS that stores courses as posts is supported. LearnDash, Tutor LMS, LifterLMS, and Sensei all work, including their meta for prerequisites, lesson count, and progress. Custom LMS-specific fields like LearnDash certificates or Tutor LMS quiz settings flow into context as named variables.

It links to your existing checkout. Most course creators send students to a WooCommerce or LMS checkout page rather than collecting payment in the chat, which keeps your transaction data, refund flow, and tax handling in one place. The chatbot acts as the discovery and qualification layer; the LMS or WooCommerce handles the enrolment and access provisioning.

SleekAI is context-aware. It can read the logged-in user's data, including their enrolled courses, progress, and quiz scores, and answer 'what should I do next' personally. For paid communities or cohort programs, this is one of the highest-value uses of AI on an education site, since it reduces TA load and gives students immediate guidance between live sessions.

Yes. Multibot lets each course have its own assistant with that course's curriculum loaded in. Useful for paid student communities where deep, course-specific Q&A is the value proposition. Each chatbot can have its own persona, system prompt, and field access, so a beginner course bot and an advanced certification bot can coexist with appropriate tone and depth.

SleekAI uses OpenAI Files as a vector store, so even a 200-page curriculum is searched accurately without bloating the prompt. Only the most relevant chunks are pulled into each conversation based on the student's question. For courses with extensive readings, lecture transcripts, and supplementary materials, this is the architecture that keeps answers grounded without hitting context window limits.

Yes. If a prospect writes in Spanish, German, or any language the model supports, SleekAI replies in that language, with course details from your English catalog translated on the fly. For platforms with translated landing pages via WPML or Polylang, the bot reads whichever locale matches the visitor's URL. Course names usually keep their original form so students can match what they discussed in chat to the catalog page.

Yes. Cohort start dates, time zones, weekly commitment, and capacity all live as meta on the course post or on a related cohort CPT. The bot reads those and answers prospect questions about the next available start date and remaining seats. For waitlist flow, the bot can collect email and link to your waitlist form, then your team handles the cohort assignment in the LMS.

If quiz and assignment data is stored on the course or student record, the bot can reference it, though most platforms keep that data inside the LMS quiz module rather than exposed as standalone meta. For homework support and concept clarification, the chatbot is highly effective; for grading or assessment integrity, configure the system prompt to redirect students to instructors rather than answering exam-style questions directly.