eCommerce Use Cases

Product Recommendation Agent

Recommend the right product in the conversation, not just on the page

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Features Leveraged

  • Behavior & Purchase-History Analysis

  • Real-Time Inventory-Aware Filtering

  • In-Conversation Upsell/Cross-Sell Logic

  • Multi-Channel Recommendation Delivery

  • Recommendation Performance Tracking

  • Live Product & Pricing Sync

Static Recommendations Miss the Moment

  • Recommendations Go Stale

    Product-page widgets often suggest items that are already out of stock or discontinued.

  • No Conversational Context

    A customer chatting about "something for a beach trip" gets generic bestsellers, not a relevant match.

  • Missed Upsell Windows

    The best moment to suggest an add-on is mid-conversation, not after checkout in a follow-up email.

How It Works

  • Customer signal received

    A question in chat, a product view, or a completed purchase triggers the recommendation engine.

  • Context assembled

    Combines browsing behavior, purchase history, and stated preferences from the conversation.

  • Live inventory filter applied

    Recommendations are checked against real-time stock and pricing before being surfaced — nothing out-of-stock gets suggested.

  • Recommendation delivered in conversation

    Suggested naturally within the chat, not as a separate widget the customer has to notice.

  • Outcome tracked

    Click-through, add-to-cart, and purchase are logged to measure which recommendations actually convert.

The Real Impact for eCommerce Teams

Metric Before Automation After (With AI Agent)
Average order value on assisted sessions Baseline +15–22%
Out-of-stock items recommended Common with static widgets Zero
Recommendation delivery point Product page only In-conversation, any channel
Recommendation relevance (stock accuracy) 75% 99%+

Step-by-Step Setup

  1. Step 1

    Connect your product catalog and inventory source (Shopify, WooCommerce, Magento, or custom feed).

  2. Step 2

    Agent builds an initial recommendation model from your catalog and available purchase history.

  3. Step 3

    Start surfacing recommendations in chat and tracking performance.

  4. Step 4

    Review conversion lift and refine recommendation rules.

Turn Every Conversation Into a Chance to Upsell

Let AI recommend what's actually in stock, actually relevant, and actually likely to convert — in the moment the customer is already talking to you.

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Frequently Asked Questions

A widget shows the same static suggestions regardless of context and can recommend items that are out of stock. This agent filters against live inventory and pricing, and delivers the recommendation inside an actual conversation — where it can also explain the fit ("this pairs well with what you bought last month") rather than just listing items.

No, every recommendation is checked against real-time inventory before being surfaced.

Yes, recommendation triggers can be configured at multiple points: browsing, cart, and post-purchase follow-up.

Browsing behavior, purchase history, and any preferences stated in the conversation — combined with live catalog and stock data.

Shopify, WooCommerce, Magento, and your product feed — plus 2,700+ integrations more broadly through the platform.