Course advisor chatbot for your catalog
SleekAI adds an AI chatbot for Course Advisor Bots to your WordPress site. It answers from the content you choose, such as posts, pages, custom post types, post meta, taxonomies and user data, with your own OpenAI, Anthropic, Google, or OpenRouter key.
Course catalogs assume the student already knows the right level
Most online and continuing-education catalogs are organised by department, not by student goal. A prospective student lands on the site with a vague intent (“learn data analysis”, “prepare for a career switch”, “upskill before the new role starts”) and gets thirty courses sorted by code.
The dropdown filters offer level and length, both of which require the student to already know what level they’re at. Advisor time is the gating resource, and most prospects bounce before they reach it.
SleekAI grounds the conversation in the actual catalog. Course CPTs, prerequisite relationships, schedule and cohort dates, instructor bios, and any ACF field your academic team maintains flow into the prompt context via the data-source wizard. The bot asks about the student’s goal and background, recommends one or two courses that fit (or a sequence if a single course isn’t right), and explains why each is the right next step.
Features
Goal-first recommendations
Most catalogs sort by department; students arrive with goals. The bot maps the goal (career switch, upskill, refresher) to a course or a sequence and explains the why, which is the work an advisor does manually for every prospect today.
Prerequisite-aware
Prerequisites live as related-post relationships or ACF references.
Answers from your data
Map post titles, content, post meta, taxonomies or a WP_Query into the instruction, and the bot answers from what your site actually publishes.
Where institutions use SleekAI as advisor
Continuing education
Universities and colleges with broad continuing-ed catalogs where most prospects can’t navigate the level structure.
Bootcamps and cohorts
Cohort-based programs where the next intake date and prerequisites determine which program fits the student’s timeline.
Corporate training
Internal L&D catalogs where role-based learning paths matter.
How SleekAI runs as a course advisor
Map the catalog
Include course CPTs, prerequisite relationships, schedule data, instructor bios, and any pathway taxonomy.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Set display conditions
Decide which pages, post types, roles or logged-in states show the chatbot.
Read the gap logs
Weekly, scan for goals the catalog didn’t serve well. The cluster becomes a program-development backlog: missing tracks, scheduling gaps, prerequisite pages that need clearer language. Advising data drives curriculum decisions.
Why advising is the bottleneck the catalog can’t solve
Catalog-based discovery assumes the prospect already knows the right level, length, and format for their goal. That assumption is wrong for most prospects, which is why advising exists as a role. The advisor’s job is to translate the student’s articulated goal into a coherent course or sequence using the same prerequisite, schedule, and difficulty data the catalog already shows but in an unhelpful order.
Advisors are also the funnel’s gating resource. Every dollar spent on marketing eventually queues at the advising desk, where response time and scheduling decide which prospects convert and which churn. The conversation that needs to happen for a good fit can’t happen at the speed marketing fills the pipeline, which is why most institutions lose a meaningful share of qualified inbound at the advising step.
The throughput effect is direct: advisors handle fewer routine recommendations and more nuanced cases, which is the work that requires their expertise. The conversion effect is bigger and harder to measure. A prospect who got a coherent recommendation within minutes is materially more likely to enrol than one who waited two business days for a callback.
The third effect is on curriculum and program development. Goals the catalog couldn’t serve well cluster into a prioritised list of new tracks, scheduling decisions, and prerequisite-page rewrites. Programs evolve faster, the catalog tightens around real demand, and the next year’s intake starts from a smarter baseline than gut-feel calendar planning ever produced.
Frequently asked questions
How does it handle prerequisites?
Prerequisites live as related-post relationships or ACF references on each course. The system prompt tells the bot to surface the foundation course when a prospect describes a background that hasn’t covered the prereqs, and to refuse to recommend an over-leveled course.
What about credit and accreditation questions?
These are accuracy-sensitive and the prompt should require grounded answers only. Credit-transfer policy, accreditation status, and equivalency rules should live on a dedicated page that the bot quotes verbatim, with a referral to the registrar for any case that goes beyond the page. The cost of inventing an accreditation claim is too high to leave to a generic generation.
Can it handle payment plans and scholarships?
Yes, when the policy lives on the site as a page or ACF field. The bot can quote the available plans (“3-payment plan, no interest”, “income-share agreement for eligible programs”) and refer the student to financial aid for case-by-case discussions.
How is this different from a typical course finder?
Course finders are filter-based: pick a level, length, format, then browse. The structural problem is that prospects rarely know which level they need or which length fits their constraints. The advisor bot inverts the workflow: the student describes the goal, the bot picks the level and length, and the student validates the recommendation rather than constructing it. Conversion data on advising flows consistently favours the latter shape.
Which AI providers does it support?
OpenAI, Anthropic, Google and OpenRouter, plus custom OpenAI-compatible endpoints. You bring your own API key and the provider bills you directly.
Where does the chatbot get its answers?
From the WordPress data you map into its instruction: post titles and content, post meta, ACF and Meta Box fields, taxonomies, terms, users and user meta, or a WP_Query. Values resolve on every request.
Can it act on a visitor’s behalf?
No. A chatbot answers questions.
Can I control where the chatbot appears?
Yes. Display conditions cover post type, post, post status, post template, taxonomy, term, user role, logged-in state and WordPress conditional tags such as front page, archive, search and 404.
Pricing
Explore our flexible licensing options tailored to your needs. Upgrade your license anytime to access more features, or opt for a lifetime license for ongoing value, including lifetime updates and lifetime support. Our hassle-free upgrade process ensures that our platform can grow with you, starting from whichever plan you choose.
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What’s included
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