Retrieval augmented chat that reads your real WordPress data
SleekAI adds an AI chatbot for Chatbot With RAG 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.
RAG without the vector stack
SleekAI gives you the retrieval half of RAG without the infrastructure tax.
Generic vector based RAG still has its place when you need fuzzy semantic match across millions of unstructured docs. Most WordPress sites do not. They have a few hundred help articles, a product catalog, and a changelog. Direct retrieval is faster, cheaper, and easier to debug. SleekAI gives you a real RAG architecture that fits the shape of an actual WordPress site instead of forcing you to operate one.
Features
Grounded in live content
Editors update docs the normal way and the next chat turn sees the change without any pipeline running in the background.
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 SleekAI RAG shines
Help center bots
Ground every answer in your published help articles. The bot quotes the live doc, links the permalink, and never invents a feature that does not exist or a policy that was retired three releases ago.
Product catalog Q&A
The bot answers comparison questions across the catalog without anyone exporting the whole shop to a vector store.
Developer documentation
Map your docs post type, code snippets, and API reference fields into the bot. Engineers ask ‘how do I authenticate the JS API’ and get a grounded answer pulled from the current docs version, not a model’s hallucinated guess.
How SleekAI implements RAG without vectors
Map content as variables
Declare which post types, postmeta keys, or taxonomies the bot can read. The mapping is the entire retrieval configuration. No pipeline, no embeddings, no reindex schedule.
Retrieve per turn
Limits, sort orders, and filters keep the result tight.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Log and tune
Adjust the variable mapping or add a new source and the next turn benefits immediately.
Why direct retrieval often beats vectors
RAG became a buzzword because grounding model output in real data fixes most hallucination problems. For a typical WordPress site, the cost is hard to justify. The same site already has a perfectly good database with structured content, indexed search, and editors who keep it current.
Direct retrieval against that database delivers the grounding benefit without the operational overhead. When a chat reply quotes the wrong thing, you can trace it back to a query in seconds. When content changes, the next turn sees the change with no pipeline running.
Vector search still has a real role for fuzzy semantic match across unstructured corpora at scale. That role rarely applies to a 500 article help center, a product catalog, or a docs site. Forcing those use cases through a vector stack is engineering theater.
SleekAI lets you build a real RAG architecture that fits the shape of your site instead of forcing your site to fit the shape of a generic RAG framework. Start simple, ground every reply in live data, and add complexity only when the volume actually demands it.
Frequently asked questions
Is this real RAG or just a marketing label?
It is real retrieval augmented generation. On each turn, the bot retrieves source rows from your database and injects them into the model call.
How does retrieval choose which rows to include?
You define the mapping. The mapping is declarative so it is easy to tune and audit.
What about privacy of retrieved content?
Retrieved rows never leave your server until they are passed to the model provider via your own API key. There is no SleekAI middleman storing your knowledge base.
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. It does not submit forms, create bookings, open tickets or send email; put the links for those steps in its instruction so it can point visitors to them.
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.
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.
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