AI chatbot for JetSearch: answers, not search results
SleekAI reads the same JetSearch query, post type filters, and JetEngine CPT data that the JetSearch widget shows, then answers in chat with direct product or article references using your own OpenAI, Anthropic, Google, or OpenRouter key.
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Search is a question, not a query string
JetSearch is Crocoblock's AJAX live-search widget for Elementor. It runs a WP_Query against the post types you configure, formats hits through a custom listing template, and shows them under the search input as the visitor types. It works well as long as the visitor knows the exact product or article name, but it is still a search engine: a ranked list of links, with no synthesis and no follow-up.
SleekAI uses the same underlying data: wp_posts, wp_postmeta, and any JetEngine CPTs you have JetSearch indexing. When a visitor types a question into the chat instead of the search bar, the bot runs a comparable query, reads the top matches, and answers in plain language with links to the relevant entries. It respects JetSearch's post type whitelist, JetEngine meta_query rules, and any custom JetSearch templates already in place. The result is the same retrieval pipeline, with a conversational layer on top.
Generic chatbots cannot do this. They either hard-code a small set of FAQs into the system prompt or rely on a static vector index built once and forgotten. When you add a new CPT entry, edit a product description, or change a JetEngine field, a generic bot is stale until you re-index. SleekAI reads live WordPress data on every message, so a product price change at 10 a.m. shows up in chat answers at 10:01.
Workflow
How SleekAI plugs into JetSearch
Inherit JetSearch settings
Translate questions to queries
Read top matches
Reply with citations
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A typical JetSearch-style conversation
Comparison
Generic chatbot vs SleekAI for JetSearch
Generic chatbot
- Returns a static link list, not a synthesized answer
- Cannot read JetSearch's post type whitelist or template settings
- Stale until manually re-indexed when CPT content changes
- Cannot combine multiple meta fields into a ranked recommendation
- Treats every site the same regardless of JetEngine CPT structure
SleekAI chatbot
-
Reads
wp_postsandwp_postmetausing JetSearch's whitelist -
Honors JetEngine
meta_queryand relation rules in retrieval - Re-queries live on every message, no separate index to keep fresh
-
Surfaces CPT fields like
views_scoreandseasonin answers - Plays nice with existing JetSearch widgets on the same page
Features
What SleekAI gives you for JetSearch
Synthesized answers
Instead of a ranked list of post titles, the bot reads the top matches and writes a short summary with the specific fields the visitor asked about, plus links to the matched entries for further reading.
Honors your filters
Whatever post types and meta_query JetSearch is configured to search are inherited automatically. Hidden CPTs and drafts stay hidden, and per-role visibility on JetEngine fields continues to apply.
Always live
Every message triggers a fresh query against WordPress, so adding a new CPT entry, changing a price, or correcting a meta field shows up in chat answers within seconds, with no separate index to rebuild.
Use cases
Where JetSearch sites apply SleekAI
Directories and listings
Trail, restaurant, or business directories with 500 to 10,000 entries get a bot that filters by meta fields like price range, hours, or distance and explains the top picks.
Knowledge bases
Documentation CPTs become a chatbot that answers in plain language, citing the specific article and section, instead of a ranked link list under the search bar.
Product catalogs
WooCommerce or JetEngine product CPTs gain a recommendation bot that reads specs, stock, and price fields directly and explains which option fits a stated need.
The bigger picture
Why this matters for JetSearch sites
Live search is great when the visitor already knows the title of what they want. It struggles when the question is comparative, conditional, or compound, the kind of question that ends with under 12 km, with a good view, that's open in October. JetSearch has no way to answer that.
It returns a ranked list and asks the visitor to click through, read, and compare. On a directory of 1,800 trails or 5,000 products, that is a lot of work to push onto the visitor. SleekAI answers those questions directly.
It reads the same WP_Query JetSearch would run, pulls the top matches, and synthesizes a short reply that references the specific fields the visitor cared about. The retrieval pipeline is unchanged, which means no separate index to maintain, no stale embeddings to refresh, and no second access control layer to keep in sync with JetEngine. For Crocoblock-heavy sites, that consistency matters.
JetSearch already enforces post type whitelists, JetEngine field visibility, and per-role access. SleekAI inherits all of it and adds a conversational layer on top. Cost is usually a small fraction of what a hosted SaaS chatbot would charge, because the inference is billed directly to your own API key.
Most directory and catalog sites land in the $5 to $30 per month range on a cost-efficient model, with the WordPress queries themselves adding almost nothing to hosting cost.
Questions
Common questions about SleekAI for JetSearch
It can, but it doesn't have to. Most teams keep JetSearch in the header for visitors who want the live-results experience and add SleekAI as a chat surface for visitors who prefer to type a question. Both run against the same wp_posts and wp_postmeta tables, so the underlying data is consistent.
 Typical end-to-end latency is 800 to 1,500 ms for a small site and 1.5 to 2.5 s on directories with 5,000+ CPT entries, dominated by the model call itself. The WordPress query is usually under 50 ms because it reuses JetSearch's own indexing assumptions. Streaming responses are supported so the first words appear in roughly 400 ms.
 No. SleekAI executes WP_Query through the usual WordPress capability checks, so anything hidden from JetEngine listings (private CPTs, draft posts, role-restricted meta) stays hidden from the bot. If a logged-in user has access to richer fields, the bot sees the same richer fields for that user.
 Yes. Configure SleekAI to read every post type JetSearch indexes (or a broader set), and add display conditions so the bot is available across the site. Many sites set up one global helper bot and one or two scoped bots for specific CPTs like docs or products.
 Yes. SleekAI works with WPML, Polylang, and TranslatePress. It reads the current language from the request context and queries the corresponding translated CPT entries. JetEngine and JetSearch's own multilingual support continues to apply, so language switching keeps both surfaces consistent.
 A cost-efficient model like GPT-4o-mini or Claude Haiku handles directory questions well at around $0.0002 per chat. Step up to GPT-4o or Claude Sonnet for nuanced recommendation flows. SleekAI lets you switch per chatbot, so a fast model can serve the header chat while a stronger one runs a deeper product advisor.
 Yes. If a trail CPT is linked to a region CPT through a JetEngine relation, the bot can resolve that link and reference fields on the related entry in its answers. Multi-step relations work up to whatever depth JetEngine itself resolves on the rendered page.
 A vector index gives broader semantic recall but goes stale and adds infrastructure. SleekAI's live WP_Query approach is always fresh, honors JetEngine access rules out of the box, and needs no separate database. Sites that want both can plug a vector layer in front of SleekAI through the actions API, but most JetSearch-style use cases don't need one.
 Pricing
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