AI Chatbot for Product Recommendations
Visitors describe what they need in plain language and SleekAI returns two or three matching products grounded in your live WooCommerce catalog, complete with price, stock status, variations, and a deep link to the exact SKU that fits the brief.
♾️ Lifetime License available
Filters lose buyers, conversation keeps them
Most shoppers can't translate what they want into the right facets. They know they need "a lightweight tent for solo bikepacking" but the filter sidebar asks for capacity, weight class, and pole geometry. The ones who care enough to figure that out finish the journey; the rest leave. SleekAI replaces the facet wall with a conversation, takes the natural-language brief, and returns a small ranked set of options with reasoning grounded in your catalog data.
Because WooCommerce products are just posts, the same context tools that read pages also read the shop. Variations, stock status, custom attributes, ACF fields, and category taxonomies all flow into the prompt. The bot can recommend by use case, compare two SKUs on weight or compatibility, and check stock in the same reply. Display conditions keep it scoped to shop and category templates so it doesn't appear on the blog or the support docs.
The conversation log is where merchandising lives. Every query that didn't surface a product, every comparison the bot couldn't make, every "do you carry X" tells you where the catalog has gaps and where the product copy is misleading. Static filters never gave anyone that visibility.
Workflow
Turn the catalog into a conversation
Connect WooCommerce data
Write merchandising rules
Place on shop pages
Audit demand gaps
Try it now
Find the right product
Comparison
Faceted filters vs SleekAI
Generic chatbot
- Requires shoppers to know the right attribute names
- Cannot reason about use cases or trade-offs
- No awareness of stock or shipping windows
- Returns dozens of results without ranking
- Static UI with no follow-up questions
SleekAI chatbot
- Reads your WooCommerce catalog including variations
- Grounds answers in live price, stock, and custom fields
- Suggests two or three picks ranked by fit
- Logs every chat so merchandising can spot demand gaps
- Display conditions show the bot only on shop and category pages
Features
What SleekAI gives you for Product Recommendations
Catalog-grounded
WooCommerce posts, taxonomies, variations, and meta flow into the prompt automatically, so recommendations always match the live catalog rather than a stale index.
Conversational filters
Shoppers describe trips, room sizes, or skill levels and the bot maps those to your tagged attributes, then surfaces a short ranked set with reasons.
Conversion logs
Every chat is searchable. Failed queries flag missing categories or unclear copy; successful comparisons show which attributes actually drive purchase decisions.
Use cases
Where merchants use SleekAI for recommendations
Outdoor gear
Match weight, packed size, and weather rating to a trip description like "three-day shoulder season ride" - and link to the SKU that fits the brief.
Beauty and skincare
Map skin type, sensitivities, and concerns to the right routine and SKUs, with cross-sells when the routine needs a serum and a moisturiser to work.
B2B catalogs
Help engineers find the part with the right spec from a 5,000 SKU catalog by describing the application instead of guessing the right filter combo.
The bigger picture
Why conversational discovery beats faceted filters
Faceted search optimises for shoppers who already know the vocabulary of your catalog. The serious bikepacker knows what "single-wall" means; the first-time buyer doesn't. As stores expand into adjacent customer segments, that vocabulary mismatch becomes the leak.
A conversational interface sidesteps the problem because the shopper describes the goal in their own words and the bot maps that description to attributes you've already tagged. The other lever is trust: a recommendation with a one-line reason - "the Ridge Bivy packs to 28cm and fits most handlebar harnesses" - beats a list of twelve unranked results because it does the comparison work the shopper would otherwise outsource to a third-party blog. Stores that solve discovery this way also gain a feedback loop they didn't have before: every miss is a logged query that points at a missing category, an unclear product title, or an attribute that needs to be tagged.
None of that data exists in a static facet UI.
Questions
Common questions about SleekAI for Product Recommendations
Yes. Products are just posts, so the same context tools that read pages also read your catalog. Variations, taxonomies, stock meta, and any ACF or product attribute can be included in the prompt. There's no separate WooCommerce-specific bridge to install or maintain.
Yes. Stock status is part of the product meta and is included in the prompt when you toggle the relevant fields. The bot can favour in-stock items, mention shipping windows for backorders, and suggest variants when the requested size is sold out, as long as that data lives on the product.
The bot is grounded in your catalog and the system prompt should tell it to refuse anything outside that grounding. With clear instructions and a tight content scope it sticks to real SKUs. If a query falls outside the catalog, the bot can offer the closest match or suggest contacting a human.
Yes. Display conditions cover the shop archive, product categories, single product, the cart, and any custom template. You can also scope by URL pattern. Most stores keep the bot off the homepage and editorial content so it only appears once a buying intent is clear.
Yes. Variations and attributes flow into the context so the bot can recommend the right size, colour, or material. It can also explain compatibility - "this strap fits the 22mm lug but not the 20mm" - when the relevant attributes are tagged on the product or its parent.
Run two bots with different system prompts and split traffic with display conditions, query strings, or a feature flag plugin. Logs are per-bot so you can compare conversion proxies like "asked about price" or "clicked through to product page" between variants.
Don't dump the whole catalog into every prompt. Use a retrieval step or a tight WP_Query that pre-filters by category and a few key attributes before context is assembled. The bot only needs the candidate set the shopper might plausibly buy, not the entire database.
Yes. Encode that logic in the system prompt - "when recommending a tent, suggest one compatible footprint and one stove if asked". Linked product meta from WooCommerce can be included so the suggestions are pulled from your existing related-product configuration rather than invented.
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.
Lifetime ♾️
Most popular
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
€749
Continue to checkout