Retail Case Study

Background

A leading auto parts distributor is exploring ways to enhance customer, supplier, and employee interactions through an AI-powered Natural Language Query (NLQ) assistant. With over 75,000 parts across a vast retail and franchise network, efficiently finding the right parts, checking availability, and accessing relevant information remains a challenge.

To address this, the company is considering a Digital Transformation initiative that integrates an AI-driven virtual assistant, enabling users to interact with structured and unstructured data through natural language queries.

Potential Objectives

Allow customers and employees to search for vehicle-specific parts.
Suggest alternative parts where applicable.
Provide real-time availability across locations and ordering options.
Retrieve pricing details for requested parts.
Extend future capabilities to include installation guides, logistics, supply chain insights, and regulatory information.

Proposed Solution Architecture

The Natural Language Query Assistant, developed by SideKick Lab, would be structured across four core layers:

User Interface Layer
- Query Input: Users can type questions in English via web-based and e-commerce platforms.
- Formatted Responses: Responses are customized for part-related data, ensuring clarity and relevance.
Administration Layer
- User Authentication: Role-based access control linked to enterprise authentication.
- Query Management: Monitors query usage to optimize performance and prevent misuse.
Logic Layer
This core AI engine would handle:
- Responsible AI Checks: Ensuring ethical and unbiased responses.
- Query Analysis: Determining relevant data sources (structured databases, APIs, or LLMs).
- SQL & API Query Processing: Translating natural language requests into structured database or API queries.
- Retrieval-Augmented Generation (RAG): Integrating unstructured content like manuals and documents.
- LLM Integration: Utilizing AI models to generate responses when required.
- Response Coordination: Combining data sources for an accurate and context-aware answer.
Data Layer
- Structured Data: Product databases, inventory, and pricing data.
- Unstructured Content: Manuals, installation guides, and other documents.
- Third-Party Content: External data sources relevant to supply chain and industry trends.

Deployment Potential: The initial implementation could focus on the internal intranet and front-of-store e-commerce platforms, with future expansion into call centers, mobile apps, and WhatsApp.

Screenshots

Below are examples of how this AI assistant can streamline the process of finding and ordering parts, while integrating with existing databases and e-commerce systems.

Expected Benefits

Enhanced customer experience by simplifying part searches and availability checks.
Improved employee efficiency with instant access to critical data.
Optimized inventory management with real-time stock insights.
Scalability to support multiple channels without modifying the core assistant.

Conclusion