AI-Powered Customer Support Assistant
Leveraging advanced generative AI to enhance customer interactions across various channels.
Project Overview
The AI-Powered Customer Support Assistant leverages advanced generative AI to understand and respond to customer inquiries across various channels. By integrating seamlessly with existing support systems, it offers personalized, context-aware assistance, ensuring efficient and effective customer interactions.
Key Outcomes
- LLM integration with multi-modal inference support
- Fine-Tuning the model with existing knowledge base
- Contextual understanding across multiple interactions
- Seamless handover to human agents for complex issues
- Integration with existing CRM and helpdesk software
Key Challenges
Scaling Support Operation
Managing high volumes of customer queries without increasing operational costs.
Natural Language Understanding
Ensuring accurate comprehension of diverse customer queries across various contexts and languages.
Human-AI Collaboration
Ensuring a seamless transition between AI and human support for complex issues.
Knowledge Base Integration
Integrate existing diverse data source(Documents & FAQ, Previous Interactions, Customer Feedbacks) and enhancing with context.
Integration with Existing Systems
Seamlessly connecting with current CRM and helpdesk platforms while maintaining data consistency.
Solution
Generative AI-Powered Responses
Leveraged Large Language Models (LLMs) to provide context-aware, natural language responses.
Hybrid Support Approach
Developed functionality for seamless handover to human agents when needed, ensuring uninterrupted service.
Enhanced Contextual Awareness
Integrated data from multiple sources, including FAQs, past interactions, and customer feedback, to enable a deeper understanding of customer needs.
Seamless CRM Integration
Enabled the assistant to interact with existing customer relationship management (CRM) and helpdesk platforms for a unified experience.
Monitoring & Control
Admin App to control and monitor the chat support operations.
Solution Approach
Research and Planning
Analyzed client requirements and existing support workflows to define project scope and objectives.
Data Engineering
Build a data pipeline to prepare the data for the LLM fine tune.
LLM Configuration
Fine-tune LLM on the existing knowledge base(FAQ, Support interaction history, Context, Feedbacks).
Chat UI
Created an intuitive Chat user interface with multi-modal input and integration with LLM.
Admin App
Admin App development to configure and monitor the chat support operations.
Technical Integration
Ensured seamless integration with existing CRM and helpdesk platforms.
Testing
Conducted rigorous testing to ensure reliability and performance before deployment.
Deployment
Deployment to the AWS Cloud and build scalable infrastructure to support cost-effective and reliable system.
Security and Compliance
Data Encryption
Ensured all data is encrypted during transmission and storage.
Access Controls
Implemented strict access controls to safeguard sensitive information.
Compliance
Adhered to relevant data protection regulations and standards.
Technology Stack
OpenAI
LangChain
NextJS
Apache Spark
AWSResults
Enhanced Customer Satisfaction
Achieved higher customer satisfaction scores due to prompt and accurate assistance.
Reduced Response Time
Improved customer response times by automating routine inquiries.
Operational Efficiency
Reduced the burden on human agents, allowing them to focus on high-priority issues.
Improved Scalability
Ensured the system could handle peak customer interaction volumes seamlessly.
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