Unilever Revolutionizes E-Commerce with Multi-Agent AI Customer Engagement
Unilever, a global consumer goods company, faced the challenge of complicated website navigation and low conversion rates due to customers struggling to find products and information efficiently. In partnership with Capgemini and Microsoft, Unilever implemented a multi-agent AI system built on Microsoft Azure, orchestrating a suite of specialized AI agents to deliver seamless, contextual, and multi-language customer experiences on their e-commerce platform. The multi-agent system leverages Azure AI, Azure OpenAI (GPT-4), Azure AI Search, Cosmos DB, and PromptFlow to power natural language interactions, personalized product recommendations, and adaptive global language support. An orchestrator agent intelligently routes queries to the right agent, while retrieval-augmented generation (RAG) enables the system to combine internal product and recipe data with external generative models for accurate, real-time responses. Key safety guardrails were implemented, including robust content moderation and prompt restrictions, ensuring secure interactions across use cases. The solution not only enhanced discovery and recommendation features but also improved order fulfillment by integrating with back-end shopping cart workflows. Notable business outcomes include faster and more accurate query responses, increased user engagement, improved conversion rates, and a frictionless global user experience, setting a new e-commerce standard for digital customer care.
- Organization
- Unilever
- Industry
- Consumer & Food
- Location
- United Kingdom
- Published
- January 2025
Reported outcomes
Strategic outcomes
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Unilever
- Provider
- Microsoft
- Maturity
- Scaled Production
- Linked source
- linkedin.com
Manual support processes could not provide fast, contextual, and personalized customer service at scale
Primary read
Use case focus
Showing 3 of 3
- 1Multi-Agent AI E-Commerce Engagement
- 2Automated Product Recommendation and Discovery
- 3Retrieval-Augmented Customer Query Resolution
- Customers spent too much time searching for relevant information on the Unilever e-commerce website.
- Complicated search processes and disconnected product discovery reduced customer engagement and conversion.
- The previous system struggled with adaptive, multi-language support, impacting global accessibility.
- Manual support processes could not provide fast, contextual, and personalized customer service at scale.
- Developed and deployed a multi-agent AI system leveraging Azure AI, Azure OpenAI (GPT-4), Azure AI Search, Cosmos DB, and PromptFlow.
- Implemented an orchestrator agent to route queries to task-specific specialized agents for product information, recommendations, and order fulfillment.
- Enabled retrieval-augmented generation (RAG) to combine structured internal data with generative AI for real-time, accurate answers.
- Built robust AI guardrails and safety layers to ensure content integrity and responsible AI usage.
- Launched adaptive multi-language support, allowing users to switch languages seamlessly in conversations.
- Faster, more accurate responses to customer queries.
- Dramatically increased customer engagement and satisfaction.
- Improved product discovery and higher conversion rates on e-commerce platform.
- Seamless shopping experience, supporting multi-language, global use cases.
Architecture
The system architecture features an orchestrator agent that receives customer queries and routes them to specialized agents based on intent. Azure AI Search and Cosmos DB store and retrieve structured and unstructured knowledge, which is passed through PromptFlow and combined with OpenAI GPT-4 models via RAG workflows for generation of answers. Adaptive language support is handled by integrating language models with user context, and all user interactions are managed via a secure Azure cloud infrastructure.
Implementation partners1
Sources & evidence1
- Customer explicitly identified
- Deployment status explicitly supported
- Technical implementation details available
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