Knowledge base search chatbot for your docs
SleekAI reads your knowledge base CPT, FAQ entries, and supporting pages, answers in the article’s own words, and cites the source so readers can confirm. Bring your own OpenAI, Anthropic, Google, or OpenRouter key.
Keyword search doesn’t speak the same language as readers
Most knowledge bases are indexed by the words the writers chose, not the words readers type. A reader looking for “how to change the plan” gets a results page that ranks an unrelated article about subscription tax because it matches the word “plan” twice in the body. Search analytics show a high query volume and a low click-through, which is the leakiest funnel in the whole product.
SleekAI replaces the search-result wall with a conversation grounded in the same knowledge base. The KB CPT, FAQ blocks, glossary terms, and any ACF metadata your editors maintain flow into the prompt context via the data-source wizard. The bot answers the reader’s question in plain language, cites the source article, and offers a link rather than dropping them into the next maze of results.
Features
Answer with a citation
Every reply names the source article and links to it. Readers get the answer in plain language at chat speed and can verify against the canonical page, which is the difference between a useful answer and a confident hallucination they later regret.
Scales with the corpus
The standard data-source wizard covers smaller libraries directly via WP_Query.
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 teams use SleekAI for KB search
Developer docs
API references, recipes, and tutorials grounded as a vector-store dataset.
Internal help portals
Operations playbooks, HR policies, and onboarding docs that live in WordPress. Display conditions on user role mean the bot scopes to the asker’s department and seniority, which keeps the answers concrete rather than generic.
Educational platforms
Course catalogs, syllabi, and policy pages grounded together. Students get answers about prerequisites, deadlines, and grading policy without spelunking through the same five PDFs every term.
How SleekAI runs as KB search
Ingest the KB
Map the KB CPT, FAQ entries, glossary, and any version taxonomy. For large libraries enable the OpenAI Files vector store; for smaller ones, direct WP_Query into the prompt context is enough.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Tune scope per section
Multibot lets a developer-docs bot and a billing bot run with their own grounding.
Set display conditions
Decide which pages, post types, roles or logged-in states show the chatbot.
Why grounded search beats keyword ranking
Keyword search optimises for documents that mention the query terms most often, which is rarely the document the reader actually wants. The reader’s question is phrased in their words; the answer lives in the writer’s words; and the match is a coincidence the search engine has to bridge.
Most knowledge bases bridge it poorly. The leak shows up as high search volume, low click-through, and a steady stream of tickets that begin with “I couldn’t find” followed by a question the docs answer in two lines. A grounded chat replaces that bridge with a translation layer.
The bot reads the corpus in the writer’s words and produces an answer in the reader’s words, with a citation that lets the reader confirm. Both sides win: the writer’s careful page is reused without rewriting; the reader gets the right paragraph at chat speed.
The compounding effect is on the corpus itself. The work that ranks the corpus higher and the work that improves the chat answer are the same work, which is why teams that adopt grounded chat tend to keep tightening the docs at the same pace their support inbox tightens.
Frequently asked questions
Does it actually replace search?
Most teams keep the search input for power users and add the bot as the primary path for everyone else. The two coexist: search returns the result list a developer browsing a recipe expects, and chat returns the answer with a citation that a less experienced reader prefers. The combination outperforms either alone because each handles the query shape it was designed for.
How does it handle ambiguous questions?
The system prompt tells the bot to ask one clarifying question when the asker’s intent is unclear, then answer. This is the conversational analog of facet filters in a search UI: cheaper for the asker because the question is in plain language, and more accurate because the bot already has the candidates retrieved and only needs one disambiguating signal.
Can it index multiple sites or doc types?
Yes. For multi-product companies, a per-product bot with its own grounded scope avoids cross-contamination between two products that share vocabulary.
What about versioned docs?
Version-aware grounding is a postmeta or taxonomy concern: tag each article with the version it applies to, then the prompt context filters by the version the visitor is on.
How current is the answer?
As current as the grounding source, because the bot answers from the article you just published rather than from a static model snapshot. The cited source link lets readers see the article timestamp themselves.
Can it work offline or behind SSO?
Yes. SleekAI runs inside your WordPress install, so an authenticated, SSO-gated documentation site can run the bot without exposing content to the public. The model provider call still goes to OpenAI, Anthropic, Google, or OpenRouter, which is the standard tradeoff for any chatbot in a private docs setting.
What does it cost relative to a hosted KB-search SaaS?
A flat WordPress plugin license, your own model provider key, and your existing WordPress docs as the corpus. Hosted SaaS options charge per article, per query, or per seat, and most ship a separate content management layer you then have to copy your docs into. SleekAI grounds against the docs you already maintain in place, which keeps the operational shape simpler.
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.
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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