AI Agents for Customer Support

Learn how AI Agents can transform your customer support processes

In today’s world, the way businesses interact with customers is evolving rapidly. AI agents, powered by advanced technologies like large language models (LLMs) and natural language processing (NLP), are revolutionizing customer support.

In the past, deploying chatbots often led to customer dissatisfaction. However, recent advancements in LLM technology have significantly improved what chatbots can do and how they engage with customers. The rise of AI agents has fundamentally transformed the landscape of Artificial Intelligence.

How can you leverage AI agents to enhance your customer support process? In this blog post, I’ll explore three powerful use cases where AI agents have delivered exceptional performance gains for our clients. Additionally, these AI solutions have helped reduce costs while maintaining, and even boosting, customer satisfaction.

Let's start!

QA Forum Chatbot

The QA Forum Chatbot project is available at Forum NOS. It showcases how AI can significantly improve customer satisfaction by providing quick and accurate answers to customer inquiries, instead of making the user look for information through tons of forum posts. This chatbot, powered by GPT and using Retrieval Augmented Generation (RAG), is publicly available for everyone that needs to obtain information about NOS (a telco company's) services.

Key Features:

Technology: Large Language Models, Natural Language Processing, Vector Databases, Retrieval Augmented Generation.
Functionality: The chatbot answers questions based on Fórum NOS articles, effectively serving as a first-line customer support tool.
Objective: To test generative AI technologies in a commercial setting with lower impact, allowing for free experimentation and refinement.
Guardrails: Public-facing chatbots require extra care when deployed. Particularly, weak implementations of guardrails can lead to PR nightmares.

This project marked one of the first public-facing generative AI chatbots in Portugal (by a private company), setting the way for more sophisticated implementations in the future.

If you have a knowledge base you'd like to make accessible to your customers through a chat interface, today’s technology makes this entirely achievable.

In-App Chat: Reducing Contact Center Interactions

The In-App Chat project aims to diminish the number of interactions that reach human assistants. This chatbot, integrated into the Woo application, answers user queries by leveraging a set of frequently asked questions (FAQs) and RAG. It is currently achieving an 80% answer rate accuracy, routing most of the incorrect answers (complex cases) to human agents.

Key Features:

Technology: Large Language Models, Natural Language Processing, Retrieval Augmented Generation.
Functionality: Users interact with the chatbot through text, receiving answers based on several FAQs in-app.
Objective: To reduce the number of contacts reaching human assistants, reducing waiting time for customers.

In such a critical process, how are we measuring business value? Through these KPIs:

Handover Rate: How many customers are routed to human agents.
Resolution Confirmation Rate: How many customers confirm that the chatbot has solved the issue.
Abandonment Rate: How many customers abandoned the chat after starting a conversation.
Impact on clients calling within 24 hours

Outcome: The project is currently deployed in customers' apps. Following the KPIs everyday gives us a good overview on current performance and if the chatbot is keeping up to date with customers' queries.

GenAI Copilot: Augmentation done Right

The GenAI Copilot project focuses on enhancing the productivity of contact managers by reducing the mean call time and improving their efficiency.

This AI-powered copilot uses GPT and RAG to provide quick and relevant responses based on Knowledge Management (KM) articles, helping human agents solve customer support issues much faster.

Key Features:

Technology: Large Language Models, Natural Language Processing, Vector Databases, Retrieval Augmented Generation.
Functionality: Assists contact managers in answering client calls more efficiently.
Objective: To empower contact managers with an AI tool that helps reduce mean call time and boost productivity.
Metrics: Mean Call Time

Outcome: This project promises significant improvements in the operational efficiency of contact centers. It's an excellent example of AI-augmentation and how AI can help humans perform better at their job.

Why leveraging GenAI for customer support?

AI agents, like the ones shown here, exemplify the transformative potential of AI in customer support processes. These projects highlight several key benefits:

Improved Customer Satisfaction: AI agents provide quick, accurate, and contextually relevant responses, enhancing the overall customer experience.
Operational Efficiency: AI agents free up human assistants to handle more complex issues, thus optimizing resource allocation.
Data-Driven Insights: AI agents collect and analyze interaction data, providing valuable insights into customer behavior and preferences, which can inform future improvements.

Challenges and Considerations

While the potential of AI agents in customer support is immense, several challenges stand:

Data Quality: The effectiveness of AI agents largely depends on the quality and comprehensiveness of knowledge base.
User Trust: Building trust with users is essential. AI agents must be transparent about their capabilities and limitations to manage user expectations effectively - in the past, not caring about user trust lead to poor implementations of chatbots.
Continuous Improvement: AI systems require ongoing monitoring and refinement to adapt to changing user needs and improve their performance over time. It's also important to understand that a background in machine learning and data science by developers is still very important to improve the performance of GenAI systems.

Conclusion

AI agents are set to revolutionize customer support processes, offering efficiency, improved customer satisfaction, and awesome insights. The projects shown here demonstrate the practical applications and benefits of AI in real-world settings.

Incorporating AI into customer support is not just about adopting new technology; it's about reimagining the way businesses interact with their customers. As AI continues to evolve, its impact on customer support will only grow, making it an indispensable tool for businesses aiming to deliver exceptional customer experiences. And, as shown here, humans in the loop are very important for the most critical customer support queries.

Want to implement any of these use cases in your business and don't know where to start? Reach out to us at contact@daredata.engineering, we would love to get to know your needs!