Retail GenAI copilot
Retail · GenAI
Shipped a retrieval-augmented service assistant across 4 languages with policy guardrails within 8 weeks.
Key Result
300+ support hours saved monthly
Duration
8 weeks
Team
2 engineers + 1 ML specialist
Technologies
6 tools
The Challenge
A global retail company with operations in 12 countries was drowning in customer support tickets. Their support team was handling 50,000+ queries monthly, with significant portions being repetitive questions about return policies, store locations, and product availability. They needed a solution that could handle multiple languages while respecting regional policy differences.
Our Solution
We built a RAG-powered customer service assistant that ingests their knowledge base, policy documents, and real-time inventory data. The system uses semantic search to find relevant context and generates responses with built-in guardrails to prevent hallucinations and ensure policy compliance. We implemented language detection and region-specific policy routing, with a human escalation path for complex queries.
Results & Outcomes
- 300+ support hours saved monthly through automated first-line response
- 85% of routine queries handled without human intervention
- Support across English, Spanish, French, and German
- Average response time reduced from 4 hours to 30 seconds
- Customer satisfaction scores improved by 18%
Technologies Used
Buyer proof path
Turn this pattern into your next shipped release.
This case study maps directly to the AI MVP Sprint engagement path, so buyers can move from proof to a scoped delivery conversation without guessing which offer applies.
Proof signals
- Knowledge retrieval grounded in policy
- Multi-language support flow shipped
- Human escalation path preserved
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