Key Technologies Under the Hood of AI Assistants for Customer Support

AI assistants and copilots have become quite common in our lives. ChatGPT and Microsoft Copilot are just two examples of the tools that are used by both professionals and the curious for handling everyday tasks. These tasks include creating texts and pictures, summarizing information, coming up with ideas for advertising campaigns, even analyzing complex data and writing the programming code.

An end user is rarely interested in the technologies under the hood of the tools that help them tackle tasks. What tool would you prefer for text summarization, ChatGPT or Bard? It depends on the task-specific and individual preferences. You will hardly dive deep into which model is better, GPT-4 or Gemini Pro.

It’s a different matter in business. Let’s assume that you choose AI copilot for your customer support. Which type of model your copilot will be based on matters. Let’s use one rather complex ticket as an example to understand the capability difference between two types of models: built on Recurrent Neural Network (RNN) and the Transformer architecture.

The query is as follows:

"Last week, I purchased a blue jacket from your online store. However, when the package arrived yesterday, the jacket was green. I've checked my order confirmation email, and it clearly states that I ordered a blue jacket. Could you assist me in exchanging this for the correct item, and also, could you confirm if the return shipping costs will be covered due to this error?"

Here is the hypothetical answer from the RNN model:

"Hello, thank you for reaching out. To exchange an item, please visit our returns and exchanges page. You can find the link on our website. Thank you for shopping with us."

Here is the response from a Transformer Model:

"Hello, I apologize for the inconvenience caused by the color mismatch in your order. Yes, we will assist you with exchanging the green jacket for the blue one you originally ordered. Additionally, since this was our error, we will cover the return shipping costs. Please refer to the instructions in your email to initiate the exchange process."

Even a non-tech person without the background knowledge of AI can note the difference in answers’ complexity, relevance to the request, and ability to catch the query crux.

The RNN model's response is generic and doesn't address the specific issues raised in the query. It fails to acknowledge the color discrepancy or the concern about return shipping costs. This illustrates the typical limitations of RNNs in maintaining context over longer text sequences and addressing specific nuances within a query.

In contrast, the Transformer model's response directly addresses the main points of the query: the color mismatch, the request for an exchange, and the query about return shipping costs. This response demonstrates the Transformer's ability to understand and retain context throughout the query, correctly interpret the nuances, and provide a more accurate and context-aware response.

Both answers are good, in a general sense, aren’t they? However, the “better” responses at scale ensure higher retention rates for your business. Hence, it makes sense to find out which models and architectures are better and why.

AI Tools Capabilities in Customer Support: From Early Days to Cutting-Edge Innovations

The Evolution of Technologies

The ultimate goal of all efforts in the field of AI, and Generative AI in particular, is to create an AI agent (tool, copilot, assistant) that can be as intelligent as a human in dealing with a vast range of tasks, such as understanding natural language, reasoning, problem-solving, creative tasks like writing and art generation, scientific research, medical diagnosis, autonomous decision-making, and even emotional intelligence for empathetic interactions with humans. (An even more ambitious goal is set in the Artificial general intelligence (AGI) domain. It’s creating a system that is not only as smart as a human but can obtain new skills autonomously. For example, the model created for image recognition can learn how to visualize data.)

As customer support is at the front and centre of our research, we’re especially interested in the field where AI overlaps with Natural Language Processing (NLP), i.e., the ability of AI agents to comprehend and generate human language fluently, understand user intent, and provide context-aware responses.

Let’s make a brief historical overview of the approaches to NLP tasks provided by different AI technologies. Our goal is to see how the technologies evolved; in other words, we’ll see how AI assistants were gaining more and more “intellect”. We’ll base our retrospective on prompt and answer examples for clearer understanding.

We’ll use the question about the blue jacket as an example once more:

"Last week, I purchased a blue jacket from your online store. However, when the package arrived yesterday, the jacket was green. I've checked my order confirmation email, and it clearly states that I ordered a blue jacket. Could you assist me in exchanging this for the correct item, and also, could you confirm if the return shipping costs will be covered due to this error?"

If you look through the table, you’ll notice that approaches and technologies evolved towards enhancing three general capabilities:

• grasping the context;
• dealing with more complex requests;
• personalization.

State-of-the-art systems are really good at meeting these criteria. However, they aren’t perfect, and AI tools’ creators still face plenty of challenges. When we understand these challenges, we’ll see the nuances that differentiate one AI agent from another. All popular AI tools are powerful! Yet, they are not equally powerful.

Challenges

Even though today’s AI-powered tools based on state-of-the-art LLMs can provide fast, relevant, precise, and context-oriented responses, there are issues with their performance. Here is the list of the key challenges.

• Contextual understanding. LLMs sometimes struggle with maintaining and understanding the context of long conversation threads. Imagine that there are LLMs able to process up to 100,000 tokens (pieces of information). It’s challenging due to memory and computational constraints.
• Speed and efficiency. Providing fast responses is crucial in customer support, but complex models can be slower to generate replies.
• Personalization. Offering personalized responses that consider the customer's history, preferences, and specific circumstances. Integrating LLMs with CRM (Customer Relationship Management) systems.
• Accuracy and relevance. Ensuring that responses are not only contextually appropriate but also accurate and relevant to the query.
• Computational capacities. Advanced LLMs require significant computational resources for both training and real-time inference, posing challenges in resource allocation and cost efficiency, especially when scaling to handle high volumes of queries without compromising performance.
• Ethical and privacy concerns. Using customer data responsibly and maintaining privacy while providing personalized support.
• Seamless human handover. Smoothly transferring a conversation from the AI to a human agent when necessary.

If you come across a solution that moves towards improvements within the listed areas, it’s a solution that can deliver value to your business.

The Ways to Succeed in Customer Service: CuSupport AI Patented Approach

Now that we know what challenges LLMs and AI tool creators face, it’s time to discover how these challenges can be overcome. There are a certain number of solutions, and each development company finds its own trajectory for overcoming endeavors - within available technical capabilities, budgets, legal requirements, and ethical guidelines.

Instead of talking in general, let’s consider how AI assistants are created, using CoSupport™ AI practices as an example.

There are two reasons why it makes sense:

• the company has been in the market of AI products since 2020 and has accumulated sufficient expertise in this field;
• сompany’s AI solutions development is based on the patented multi-model message generation architecture that is an instance of an innovative approach to building complex AI products, optimized in terms of costs and computational efficiency.

Let’s take a closer look at the CoSupport™ AI solutions from the perspective of the company's patented technology, which is vital for the AI model's fast performance and response accuracy. Let’s keep in mind that the patented technology is crucial for one product – CoSupport™ Agent. Two other products, CoSupport™ Customer and CoSupport ™ BI, involve the patented technology only partially.

Before we start, let’s clarify what CoSupport™ Agent is. It’s an AI-powered assistant designed to boost the productivity of your human support staff. It’s a tool that integrates into a company's CRM and provides a human agent with a draft of a response. CoSupport™ Agent incorporation into workflow reduces ticket processing time and cuts down support costs by 30-80%.

Contextual Understanding

Context awareness in AI assistants is beneficial as it allows for coherent dialogue. Take a customer discussing a blue jacket return; a context-aware AI remembers this detail, so it knows the reference when "the item" is mentioned later. This capability prevents repetitive questions and ensures the AI recognizes and builds upon issues mentioned earlier in the thread, leading to quicker, more personalized support.

How it works in CoSupport™ Agent

Custom models trained on particular clients’ data intelligently analyze customer queries and conversation history to draft contextually relevant responses. Our AI assistant is always aware of to whom it talks, what issues were discussed in the previous conversations, and how the current ticket is connected to the previous customer experience within a company (if it’s not the first query). As a result, clients don't need to provide background information to solve their difficulties.

Speed

A swift response not only addresses customer issues quickly but also demonstrates a company's commitment to their needs. Efficient handling of inquiries allows for managing a higher volume of tickets, reducing wait times, and improving overall service quality. An AI assistant that enhances speed and efficiency can significantly boost customer experience and operational effectiveness.

How it works in CoSupport™ Agent

This is where CoSupport™ AI multi-model message generation architecture’s capabilities come into full swing. AI assistant’s “brains” differentiate three types of queries:

• The simplest ones focused on the typical issues (that are usually mentioned in FAQ sections of the website or knowledge base). To answer such requests, an AI assistant doesn’t need to address to the LLMs. The answer could be quickly found in the knowledge base or other tickets. It prevents the system from overloading and speeds up its performance.
• More complex ones, requiring “understanding” of the situation and ways to solve difficulties. When the system of the models detects such requests or sees that the question is a part of the thread, it starts the “thinking” process to provide the relevant and precise answer.
• Messages about technical issues. The system of the models processes such requests separately, which also helps optimize computational resources and save time.

CoSupport™ AI’s patented architecture ensures a considerable reduction of ticket processing time without compromising CoSupport™ Agent’s competence.

Personalization

Personalization fosters a deeper connection between customers and the brand. Tailored responses make customers feel valued and understood, enhancing their overall experience. Personalization also increases the effectiveness of support by addressing individual needs and preferences, leading to higher customer satisfaction and loyalty.

How it works in CoSupport™ Agent

Being integrated with a CRM system, an AI assistant gets access to detailed customer profiles; it enables an AI agent to personalize interactions based on comprehensive customer insights. In addition, an AI assistant maintains the context of ongoing conversations, ensuring that responses are not only relevant to the current query but also reflective of previous interactions.

Computational Capacities

Optimizing computational capacities is crucial for maintaining cost efficiency, as high computational demands can significantly increase operational expenses. Efficient computation is essential for scalability, allowing a company to handle growing volumes of customer interactions effectively. Moreover, optimized computational resources ensure faster processing, leading to reduced response times, a key factor in enhancing customer satisfaction.

How it works in CoSupport™ Agent

CoSupport™ AI’s patented multi-modal architecture allows streamlining tasks to the specific model so that each model can focus on its own field of responsibility. To compose the simple answer, minimal computational resources are required. The generative model is involved only in solving complex queries that need context-awareness and decision-making.

Accuracy and Relevance

Accuracy and relevance are critical as they directly impact the effectiveness of the assistance provided. Accurate responses ensure customer queries are correctly understood and appropriately addressed, leading to effective problem resolution.

Relevance is equally essential, as it ensures that the information provided is pertinent to the customer's specific situation and needs. High accuracy and relevance in responses not only enhance customer trust and satisfaction but also reduce the likelihood of miscommunication and the need for follow-up interactions.

How it works in CoSupport™ Agent

There are several ways in which CuSupport™ Agent provides accurate and relevant answers to requests:

• Data Integration. Integrating with CRM systems and databases, an AI agent accesses a wealth of customer data, enabling them to tailor responses to each customer’s history.
• Contextual Awareness. By maintaining the context of conversations, AI assistants provide relevant responses based on the current discussion and past interactions.
• Advanced Natural Language Processing. Utilizing custom LLM models and improved neural networks, an AI assistant can understand and interpret the nuances of human language, ensuring accurate comprehension of customer queries.
• Machine Learning and Continuous Training. An AI assistant is trained on vast datasets and continuously learns from new interactions, improving their accuracy and ability to provide relevant information over time.
• Reinforcement Learning. An AI assistant learns optimal behaviors through rewards and penalties based on its responses. This method continually improves their decision-making process, leading to more accurate and relevant customer support outcomes

One of the CustomerSupport AI’s architecture peculiarities is that CoSupport™ Agent is trained on the company's data exclusively. Unlike in GPT or other popular models, Agent’s knowledge is focused on a company, its services, and communication with clients. Thus, Agent’s responses to requests are concentrated on the issues that are key in customer service instead of being a tool for solving general tasks or amusement.

Ethical and Privacy Concerns

Ethical and privacy concerns are essential for building trust and maintaining a company's reputation, as breaches or misuse of data can have severe consequences. Compliance with privacy laws like GDPR is legally mandatory, and failure to adhere can result in significant penalties. Additionally, the ethical use of AI and robust privacy measures protect customers from potential harms such as identity theft and data breaches, ensuring the long-term sustainability of AI technologies.

How it works in CoSupport™ Agent

CoSupport™ AI team provides 100% security for data privacy. One of the models – the cleaner model – is responsible for filtering the private data so that it won’t come to the databases that will be used for model training. Today. Sixteen types of data are under security and privacy protection.

Seamless Human Handover

Seamless human handover ensures that complex or sensitive issues beyond the AI's capabilities are efficiently transferred to human agents, providing customers with the best possible assistance. It maintains the continuity and context of the customer's query, preventing frustration from having to repeat information. This handover enhances the customer experience by combining AI efficiency with human empathy and expertise. Ultimately, it strikes a balance between automated and personalized support, crucial for customer satisfaction and effective problem resolution.

How it works in CoSupport™ Agent

CoSupport™ Agent provides the optimal balance between automation and human touch. As we’ve mentioned before, the Agent implies the human in the loop: the AI assistant prepares the draft for a response, not less, not more. The final word is always the human agents’ one; and here, a person can demonstrate their full empathy, customer service experience, and overall life experience in delivering the most precise, well-formulated answer that keeps the relevant tone of voice. One important detail: with a CoSupport™ Agent, such a response will be ready ten times faster.

Conclusion

Since the early days of NLP technologies till today, when NLP tasks are empowered by AI and machine learning, the technologies have evolved tremendously.

Today’s tools, such as AI assistants, are impressively capable, excelling in tasks ranging from data analysis to personalized customer service.

However, there is much room for improvement since the AI field is experiencing childhood or early adolescence; it still has the potential to develop further. Growing demands and expectations, along with the natural inertia of development, incentivize competition among companies that provide AI solutions, including tools for customer support.

Competition plays into the hands of end-users. AI solution providers implement new insights, improve existing products, and come up with new approaches. It shapes the landscape of innovation and fosters development.

CoSupport™ AI, as a creator of AI solutions for customer support and business in general, has been in search of its own approach to AI-empowered products since 2020. In December 2023, the company was patented for its innovative approach to building a multi-model message generation architecture. Today, the pioneering technology forms the basis of the company's primary product, CoSupport™ Agent; it is also partially utilized in two other company solutions: CoSupport™ Customer and CoSupport™ BI.

The patented technology differentiates three types of customer requests and addresses each type to the different “zones of responsibility” in the neural network architecture. This differentiation is the basis for fast responses: a human agent gets the draft of the response within milliseconds. What is more significant, AI assistant’s replies are context-aware and personalized.

Since the AI model is trained on the company’s data exceptionally (unlike LLMs such as GPT or Gemini), it provides more precise answers to the queries, strictly connected to the company’s products and services.

Patent is a milestone in CoSupport ™ AI’s history. It’s a source of pride and a catalyst for working harder to offer clients more efficient, optimized, and specially designed solutions.