✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount

AI Chatbot for Content Discovery

SleekAI reads articles, podcast episodes, videos, and any custom post type, then recommends two or three follow-up pieces with a one-line reason each so readers trust the suggestion. Static related-posts widgets become a stale memory.

♾️ Lifetime License available

SleekAI chatbot for Content Discovery

Your archive is your best traffic source - if visitors can find it

Most blogs lose readers after the first article. The related-posts widget recommends by tag, which means it surfaces things the reader already knows about and ignores topic depth. Visitors arrive on a guide about email deliverability, finish it, and leave because nothing on the page told them "the next step is DMARC, and we have a 2,000-word piece on it".

SleekAI keeps readers in the archive by reading any post type - articles, podcasts, videos, courses, custom CPTs - and recommending the next thing with a sentence of context. Because the model can reason about topic depth rather than just tag overlap, a beginner-level guide on deliverability points to an intermediate piece on SPF and an advanced one on DMARC migration, in that order. Display conditions place a different recommender on the editorial blog than on the podcast index than on the course platform.

The conversation logs become an editorial backlog that reveals what readers actually want to learn next. Topics that come up repeatedly without a strong match in the archive are the next briefs. That feedback loop is the one most blogs never had, because readers who don't find what they want just leave silently.

Workflow

Turn the archive into a guided tour

1

Index every content type

Pull articles, episodes, videos, courses, and any custom post type into the prompt context. The bot ranks across all formats, so a reader looking for advanced material can be steered to a podcast or a course.
2

Write the recommender prompt

Tell the bot to suggest two or three pieces, each with a one-line reason, and to consider topic depth - not just keyword overlap. "Advanced takes" and "adjacent topics" are explicit categories.
3

Per-section bots

Multibot plus display conditions runs a different recommender on the marketing blog than on the engineering blog. Each can have its own scope, voice, and ranking rules.
4

Read the gaps

Filter logs for queries the archive couldn't answer well. The recurring topics are the next editorial briefs - and far more reliable than a keyword tool because the queries come from real engaged readers.

Try it now

What should I read next?

Visitors describe what they care about and the bot returns three relevant pieces from your library, each with a one-line reason it fits.

Comparison

Related posts widget vs SleekAI

Generic chatbot

  • Static related posts based on tags, not topic depth
  • Cannot factor in what the visitor just asked
  • No conversational follow-up to refine the suggestion
  • Ignores podcasts, videos, and custom content types
  • No insight into which recommendations actually convert

SleekAI chatbot

  • Reads any post type - articles, podcasts, videos, courses
  • Suggests with a reason, not just a thumbnail
  • Logs reveal the topics readers want more of
  • Per-template prompts so the bot behaves differently in each section
  • Plays nicely with newsletter capture once the visitor is engaged

Features

What SleekAI gives you for Content Discovery

Archive-aware

Pull posts, episodes, videos, and courses into the prompt and let the bot rank them. Any post type registered in WordPress is fair game, with custom fields included on request.

Reasoned recommendations

Every suggestion comes with a one-line explanation of why it fits, not just a thumbnail. Readers trust the suggestion because the reasoning is visible.

Editorial logs

See which topics readers ask about most and write toward the gaps. The log is a better content brief than any keyword tool because the queries come from real engaged visitors.

Use cases

Where content teams deploy SleekAI

Editorial blogs

Keep readers in the archive by routing them through depth - beginner to intermediate to advanced - rather than the same tag at the same level over and over.

Podcasts

Match listeners to the right episode based on what they want to learn, including which guest covered a topic best and which episode is the right level.

Course platforms

Recommend the next module based on the learner's stated goal and current progress, not just the curriculum order, so motivated learners can skip ahead.

The bigger picture

Why the related-posts widget is dead weight

Tag-based related posts assume the reader's next interest is in the same tag. That assumption breaks the moment your archive grows past a few dozen pieces, because the right next read is usually one tag over - the deliverability reader's next read is about authentication, not about more deliverability. Editorial sites that accept this and use AI for the recommendation see a measurable change in pages-per-session and time-on-site, but the more interesting change is in editorial direction.

The conversation logs surface the questions readers ask after finishing an article, and those questions are far more honest than any analytics dashboard. Three readers asking "is there an advanced take on DMARC" beats every keyword research tool in the planet for telling you what to write next. Podcast and video sites benefit even more, because their archives are inherently harder to skim and a conversational recommender does for audio what a smart sidebar never did for blogs.

The cost is small once the archive is indexed; the upside compounds with every piece you publish.

Questions

Common questions about SleekAI for Content Discovery

Yes. Register any post type and SleekAI can include it in the prompt context. Episodes, videos, courses, talks, ebooks, even bespoke types like "interview transcripts" all work the same way as standard posts. ACF and Meta Box fields on those CPTs flow in too, so you can recommend by speaker, by series, or by reading level.

 

If you allow it in the system prompt - many teams keep it scoped to their own archive to avoid sending traffic away. A common middle ground is to let the bot reference external sources by name when an internal piece doesn't exist yet, but never link out. That keeps the user oriented toward your archive while acknowledging the broader landscape.

 

Content flows in live from the database - no separate index to maintain. A piece you publish at 9am can be recommended at 9:01. If you cache the prompt context for performance you'll see the new piece on the next cache rebuild. Most teams set the cache window to match their publishing rhythm.

 

Yes. Multibot lets you scope a bot to a category, tag, or template, and display conditions handle the routing. The marketing-blog bot can have a different prompt and a different content scope than the engineering-blog bot, even on the same WordPress install.

 

Configure access by role or post status - paid content can be hidden, teased with a soft paywall, or fully exposed depending on the visitor's tier. For membership sites this is the same logic that gates the rest of the experience, applied to the chatbot's grounding scope.

 

Built-in logs let you filter by topic, query, and outcome. The most useful filter is "queries that didn't return a confident match" - those are the topics the archive doesn't cover well, which is exactly the editorial backlog the recommender is supposed to surface.

 

Limit the bot to two or three suggestions per reply, with reasoning, and let the user ask for more. Avoid the temptation to push a paid course or a newsletter signup in every reply. Trust comes from relevance; the conversion comes after the third or fourth genuinely useful suggestion.

 

Yes, via webhook or the JS API. A common pattern is to let the bot offer the newsletter only after two or three exchanges, when engagement is real. That converts better than the popup that interrupts the first article and respects the reader's attention.

 

Pricing

More than 1000+
happy customers

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.

Starter

€79

EUR

per year

  • 3 websites
  • 1 year of updates
  • 1 year of support

Pro

€149

EUR

per year

  • Unlimited websites
  • 1 year of updates
  • 1 year of support

Lifetime ♾️

Most popular

€249

EUR

once

  • Unlimited websites
  • Lifetime updates
  • Lifetime support

...or get the Bundle Deal
and save €250 🎁

The Bundle (unlimited sites)

Pay once, own it forever

Elevate your WordPress site with our exclusive plugin bundle that includes all of our premium plugins in one package. Enjoy lifetime updates and lifetime support. Save significantly compared to buying plugins individually.

What’s included

  • SleekAI

  • SleekByte

  • SleekMotion

  • SleekPixel

  • SleekRank

  • SleekView