Key Highlights: What This Case Study Covers

• Best practices in deploying a multilingual Gen AI assistant tailored for global retail operations
• Implementation of a scalable AI pipeline using OpenAI and Snowflake for high-volume, real-time customer interactions
• Building a future-ready conversational AI framework that supports 50K+ products and policy data in multiple languages
• Real-world application of vector search and retrieval-augmented generation (RAG) for efficient, personalized customer support
• Strategic approaches to optimize frontline sales performance by reducing manual digital engagement

Client Overview

Our client is a global retailer with a footprint spanning over 400 duty-free outlets across various international airports. Renowned for delivering high-end experiences to a diverse international traveler base, the client sought to modernize how sales associates engaged with digital inquiries across geographies and languages.

The Ask

The client aimed to amplify sales team productivity and elevate customer service by deploying a Generative AI solution capable of handling high volumes of multilingual customer inquiries in real time, without compromising personalization or accuracy.

Key Challenges

• High Volumes of Digital Queries: Sales associates had limited time to respond to frequent, routine customer questions across digital channels.
• Vast and Dynamic Product Catalog: With 50,000+ products and frequent updates to inventory, promotions, and policies, access to real-time insights was complex.
• Dispersed Information Sources: Product and policy data were spread across systems, making real-time support challenging.
• Multilingual Complexity: Addressing queries in multiple languages and ensuring consistent translation, especially for brand-specific terminology.
• Limited Context Retention: Existing systems had a limited ability to retain context across multi-turn conversations, affecting follow-up query handling.

Our Solution: Gen AI Sales Agent Assistant

Tiger Analytics built and deployed a Generative AI-powered sales assistant that integrated directly into the client’s digital customer touchpoints. The delivery was structured around four key phases:

1. Data Aggregation & Preparation

• Unified internal data sources like product catalogs, store policies, inventory data (~50,000 rows), and web content (~5–6 KB files)
• Enriched the solution with external product content via web scraping and public knowledge
• Cleaned, classified, and prepared data for vectorization—removing symbols, formatting text, and ensuring semantic clarity

2. Embedding & Retrieval Architecture

• Converted unstructured data into vector embeddings using OpenAI’s embedding API
• Stored vectors in a high-speed vector database for quick semantic search
• Structured data (e.g., Chinese-English product lookup tables) was stored and accessed via Snowflake for robust performance
• Built a prompt library to optimize retrieval and response generation logic

3. Conversational Interface & Intelligence

• Developed an intelligent chatbot UI
• Used GPT-3.5 Turbo for natural language understanding and response generation
• Enabled real-time translation and reformulation, converting Chinese queries to English, decomposing compound questions, and routing them through a query classifier
• Connected to both structured SQL queries and unstructured knowledge documents for full-spectrum responses
• Responses were reassembled, translated back to Chinese, and returned, creating a seamless multilingual experience

4. Scalable, Modular Framework

• The entire system was built with scalability as a key priority, capable of handling more product lines, additional languages, and deeper integrations
• Tech stack included OpenAI, Snowflake, Azure, Streamlit, and MongoDB, ensuring cloud-native, secure, and extensible architecture

Impact Delivered

• Over 70% Response Accuracy: Delivered high-quality answers aligned with live product and policy data
• Under 3 Seconds Latency per Query: Optimized processing for both speed and relevance in responses
• 40–60% Reduction in Manual Query Handling: Significantly cut down agent time spent on repetitive digital tasks
• Improved Customer Satisfaction: Faster, consistent responses improved NPS and reduced chat abandonment
• Sales Agent Enablement: Freed up time allowed agents to focus on personalized upselling and in-store engagement