Shopify · AI product recommender

Conversational product recommendations that actually add to cart.

Static “you may also like” carousels convert at ~1-2%. Conversational AI recommendations convert at 5-15% — because they ask the right qualifying questions, surface 2-4 SKUs with inline product cards, and add to cart in-conversation.

Clearly Agent reads your full Shopify catalog and personalizes per visitor. Free tier.

How it works

1. Catalog auto-sync — Clearly reads every product, variant, tag, collection, price.
2. Customer asks — “I need a gift for my mom’s birthday under $100” or “What shoes go with this dress?”
3. Agent qualifies — “What’s her style — minimal or maximalist?” (configurable; you decide how aggressive the qualification is)
4. Inline product cards — 2-4 SKUs with image, price, “Add to cart” button.
5. Customer adds inline — agent calls Shopify Cart API; cart updates real-time.
6. Cross-sell, naturally — “Want me to add the matching earrings too?”

What you can configure

• Qualification depth — light (1 question) or thorough (3-5) before recommending.
• Cross-sell aggressiveness — quiet (only when customer asks) to active (always offer pairs).
• Price range filtering — agent respects stated budget; never up-sells past it.
• Bundle logic — “If customer buys a top, suggest matching bottom from the same collection”.
• Sale-aware — surfaces discounted items proactively when they fit.
• Inventory-aware — never recommends sold-out variants; offers restock notifications.

ROI math

A typical Shopify store sees 1-2% conversion on static “related products” clicks. Clearly Agent conversational recommendations convert 5-15% — that’s 3-7× more orders from the same traffic.

FAQ

How is this different from Shopify's built-in product recommendations?

Shopify's native "Related products" / "Often bought with" is a static carousel based on co-purchase data. Clearly Agent is conversational — it asks qualifying questions ("for daily use or special occasion?", "what's your budget?"), then surfaces 2-4 SKUs with inline product cards and reasoning. Conversion typically 3-5× higher than static carousels.

How does it know what to recommend?

Reads your full Shopify catalog (products, variants, tags, collections, prices). Uses semantic understanding — "I need something casual for summer" maps to products you've tagged with relevant attributes. You can guide further with knowledge-base notes: "When a customer mentions sensitive skin, prioritize products tagged 'gentle'".

Does it actually add items to cart?

Yes — when the customer commits ("I'll take the medium in blue"), the agent calls Shopify's Cart API and adds the variant inline. Customer can keep chatting; cart updates in real-time. Reduces friction from "see recommendation → click product page → choose variant → add to cart" to one conversational turn.

Can it handle complex queries like outfits or routines?

Yes. "Build me a 3-step skincare routine" returns cleanser + serum + moisturizer with order-of-application notes. "Pair this dress with shoes and a bag" returns coordinated items. Configure your bundle logic in the knowledge base.

How does it handle out-of-stock?

Sees inventory in real-time via Shopify webhooks. Won't recommend sold-out variants. Suggests alternatives: "The medium is out, but the small + size up is on-trend right now" or offers restock notifications.

Will it push expensive products inappropriately?

No — it recommends based on customer-stated preferences (including budget). If a customer says "$50 or under", it filters. You can set additional guidance: "Always offer a mid-range option first" or "Surface bundles before single items".

How does it personalize across visits?

Customer memory persists per visitor (cookie-based). Once a customer tells the agent their size, style, sensitivities, etc., it remembers across sessions. Returning visitors get warmer, faster recommendations.