AI Agent for product recommendations

With Eloquent you can easily create an AI agent for webshops that recommends products based on user preferences. Instead of browsing through long category lists or endless filters, shoppers get a curated selection of products in clean, easy-to-read cards. Each journey feels unique and personal, boosting both customer satisfaction and conversion rates.

How it works

The agent is trained on your product catalog (descriptions, specs, categories) and integrates via API to fetch live product data (price, availability, variants). During a session it:

• Asks clarifying questions (needs, budget, preferences).
• Matches answers to product attributes in the knowledge base.
• Checks stock and pricing via API before recommending.
• Presents the best options in clear cards and avoids out-of-stock items.

Our recommendations

• Prefer API integration → Use a products/availability endpoint so recommendations are always current and never include OOS items.
• Cache smartly → Keep embeddings for descriptions, but always validate stock/price live.
• Make it visible & set expectations → “AI Shopping Assistant — get tailored picks in seconds.”
• Keep it fresh → Daily sitemap/RSS for content; real-time API for stock/price.

What can it do?

• Recommend products tailored to user preferences (budget, size, features, style).

• Summarize differences between similar products.

• Upsell or cross-sell by suggesting related accessories or bundles.

• Collect personal data (email, phone) to follow up with promotions.

• Trigger checkout actions or forward the selection to human sales if needed.

What pain points does it solve?

• Shoppers leaving because they can’t decide or find the right product.

• Confusing navigation and too many product options.

• High cart abandonment rates due to lack of guidance.

• Sales teams wasting time answering repetitive pre-purchase questions.

• Missed upsell opportunities when users don’t see related products.

Who it’s for

This use case is especially relevant for:

• E-commerce stores → guiding shoppers through large catalogs.

• Retail brands → helping customers pick the right model, size, or bundle.

• Consumer electronics → matching complex specs (e.g. laptops, cameras) with customer needs.

• Furniture & home goods → helping buyers choose items that fit their space, budget, or style.

• Beauty & wellness → recommending products based on skin type, preferences, or goals.

How to set it up

• Agent temperature: 0.1
• Reranking: On (large catalogs)
• Embeddings: 20
• Similarity: Default
• Integration pattern:
  • RAG for product copy/specs.
  • Function call / webhook to GET /products?in_stock=true&… (or similar) before finalizing recommendations.
  • Optional: function to fetch price, variant availability, ETA.

While standard integrations aren’t available yet, our team can support you in creating a custom one. You can reach out to [email protected].

Example in action

• Consumer electronics webshop → An agent asks about budget, screen size, and performance needs, then recommends 3 laptops, each with a clear summary.

• Beauty & skincare shop → A recommendation agent asks about skin type and goals, then suggests tailored skincare sets — plus an upsell of accessories.

In both cases, the agent increases buyer confidence, shortens the decision process, and reduces drop-offs — leading directly to higher conversion rates and bigger baskets.

This use case is one of the clearest ways for agencies to prove ROI in e-commerce — better user journeys translate directly into more sales.