Pre-sale: search, comparison, recommendation
The chatbot guides the buyer through the catalog, compares products against their criteria, and recommends the best option with context.
11 · Access International productIn progress / ready for deployment
E-commerce conversational chatbot — Conversion and support
Real-time assistance on your store: pre-sale, checkout, post-sale.
Conversational chatbot integrated into the e-commerce site, connected to product catalog, CRM, and recommendation engines. Covers pre-sale, checkout, and post-sale. Reduces abandonment rate and increases average basket. MCP-first architecture orchestrated through our ATLAS-Agentic methodology.
Goal
Convert your shop visitors into customers through an agent that guides them in real time.
How
Chatbot connected to catalog + CRM + AI recommendations + selective human escalation on pre-sales and after-sales.
Benefits
- 24/7 availability for pre-sale, checkout, and post-sale
- Measurable cart abandonment rate reduction
- Personalized recommendations connected to real-time catalog
- Smooth human escalation on complex cases (uninterrupted handover)
Access reference — already deployed in production
- Sector
- E-commerce — group product (Favizone)
- Delivery mode
- Proprietary product — multi-client integration
- Volume / scope
- AI recommendations live: +25% revenue from targeted recos, 35% email open rate, 40% click-through
- Stack
- SaaS APIs, recommendation algorithms, JavaScript site tag, XML/CSV product feeds, email automations (cart abandonment, cross-selling)
- Status
- Live across multiple clients
Reference anonymized under confidentiality. Details available on request under NDA.
Customer experience impact
Purchase comfort and NPS lift
The goal is not to push a product at all costs but to simplify the buyer's life: find quickly, understand, compare, finalize without friction. A buyer who feels guided without pressure and gets a clear answer to their question leaves the store satisfied, even without purchase. Expected impact: improved Net Promoter Score, loyalty, increased spontaneous recommendations.
Typical use cases
Marketplaces, online retail, B2C services, post-sale support, abandoned cart recovery.
Checkout: assistance and cart recovery
Helps finalize payment, answers objections in real time, recovers abandoned carts with personalized messages.
Post-sale: order tracking, FAQ, returns
Delivery status, returns management, post-purchase product questions. Reduces human customer service load by 40 to 60%.
Cross-sell and up-sell recommendations
Personalized suggestions based on purchase history and similar behaviors. Increases average basket.
Business functions served
Business functions served
This product applies to the following business functions. Click to discover how we adapt it to each.
Addressable to
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Frequently asked questions
Frequently asked questions
Compatible with which e-commerce platform?+
Shopify, WooCommerce, Magento, Salesforce Commerce Cloud, Prestashop, and most CMS via API. Custom integrations on request for ERP.
Which AI model do you use?+
Claude for complex reasoning, GPT-4o for multimodal, choice based on benchmark performance on your catalog. Switch without friction if a better model appears.
What is the measurable impact?+
On similar deployments: 18 to 35% reduction in cart abandonment rate among interacting visitors (Baymard Institute 2026 benchmarks for AI chatbots + checkout optimization), 10 to 20% increase in average basket via contextual recommendations.
How does human escalation work?+
Automatic detection of out-of-scope cases (complex complaint, personalized request), handoff to a human agent with all conversation context, no disruption for the customer.
Case studies
Related case studies
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