Customer Support Efficiency for RetailerWise Using LangChain
Overview
RetailerWise is a leading Denver-based retail chain that wanted to improve its customer support services. Since the client’s business is quite famous, they don’t want to compromise and were ready to go the extra mile. Our consultation helped them understand what can be done using modern-age technologies, and quickly, it agreed to implement whatever is required to improve customer retention and satisfaction.
Technical Stack
Industry
Retail
Region
Colorado
Project Size
Non- Disclosable
Highlights
65% Reduction in customer support response times
45% Improvement in overall customer satisfaction
70% Decrease in repeat inquiries due to enhanced response relevance
50% Growth in positive customer feedback through AI-driven support
Challenges & Solutions
Data retrieval was one of the major challenges our client faced, and it was their top priority when they approached us.
- Solution: Our AI developers are well-versed in LangChain’s retrieval-augmented generation-optimized data access, which enables the AI to sift through the vast RetailerWise knowledge base with precise keyword relevance and semantic filtering, reducing response time.
Our client was even struggling with maintaining response consistency across channels.
- Solution: Our AI developers implemented LangChain’s memory modules to ensure consistency in customer interactions across all touchpoints. This allows the AI to recall context in sequential interactions without redundancy.
The client knew exactly how to hook their customers, which is why they wanted a system that could offer them products they liked or wanted to buy.
- Solution: With the use of ElasticSearch and LangChain’s vector-based query enhancements, our AI engineers made the system quickly retrieve the most relevant product recommendations, enhancing both the accuracy and contextual relevance of each suggestion.
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Years of Business
Experience
Happy
Customers
Countries with
Happy Customers
Agile enabled
employees
Core Features
- Real-time Query Handling
- Memory-powered Contextual Flow
- Semantic Search & Retrieval
- User Feedback Loop
No. of Developers
06
Time Frame
April 2023 - May 2024
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