Retrieval-augmented answers, grounded in your own WordPress content
SleekAI adds an AI chatbot for RAG Chatbot 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.
Hallucinations come from the missing context, not the model
When a chatbot makes things up, it is almost never because the model is broken. It is because the model was asked a question with zero useful context, so it answered from training data that may be three years out of date and never knew about your business.
The RAG pattern (retrieval-augmented generation) fixes that by inserting the right pieces of your actual content into the prompt at request time. The trick is doing the retrieval well, in a way that fits how your data is shaped.
SleekAI implements RAG natively against the WordPress database.
The reason most chatbot platforms struggle with RAG on WordPress is the indirection. They pull your sitemap, scrape rendered HTML, and end up indexing menus, footers, and cookie banners alongside the actual answer.
SleekAI reads the database directly, so a product description is a product description, a docs page is a docs page, and a custom field labeled policy_text is exactly that. The retrieval is over real fields, not over noise.
Features
Native WP retrieval
No HTML scraping, no noise from headers and footers, no stale sitemap to re-crawl.
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.
Your model, your key
Use OpenAI, Anthropic, Google, OpenRouter or a compatible endpoint with your own API key. The provider bills you directly.
Where RAG genuinely beats a plain chatbot
Internal research tools
Staff query a private custom post type of research notes or internal SOPs.
Repeat questions
Answer the questions visitors ask every day from the pages and posts where you already answer them.
The next step
Put your booking page, contact form or pricing page in the instruction so the bot links visitors there when they are ready.
How RAG runs against your WordPress content
Set display conditions
Decide which pages, post types, roles or logged-in states show the chatbot.
Pick a model
Store your OpenAI, Anthropic, Google or OpenRouter key once in the shared AI settings, or add a compatible endpoint, and choose the chatbot’s model.
Review and refine
Turn on logs, read what visitors ask and tighten the instruction or the page behind it.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Why RAG belongs inside the CMS, not next to it
The case for retrieval-augmented generation is well understood at this point: hallucinations come from missing context, retrieval supplies the context, citations let the user verify. What is less understood is how much architecture you have to compromise on when the retrieval lives outside your CMS.
Reading the database directly is the only way to keep retrieval honest. SleekAI’s RAG runs against the same rows the front end renders from. RAG is supposed to make answers verifiable. Doing it inside WordPress is what makes the verification real.
Frequently asked questions
How is RAG different from just feeding the model my whole content?
Even with a large context window, dumping your entire site into the prompt is wasteful, slow, and expensive per call.
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.
Can I run more than one chatbot?
Yes. Each chatbot has its own instruction, data, display conditions and styling, so different sections of a site can get different bots.
Are conversations stored?
Only if you turn on logging for that chatbot. SleekAI then stores each visitor message and each completed reply, which you read and delete on the chatbot’s Logs page, and a retention period can delete old logs.
Does it handle large amounts of content?
Query results can be synced to OpenAI file search on a schedule, so the chatbot finds the relevant entries instead of sending everything with each request, and a retrieval priority per query or post type ranks what it finds. This needs an OpenAI key and model.
Related pages
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.
…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
SleekView
SleekRank
SleekTrack