Author: Catarina Ribeiro

Articles

AI Quality Assurance: Revolutionizing Call Center Agent Performance Evaluation

Are you still spending countless hours manually reviewing Call Center recordings to assess agent performance? It's time to step into the future with the AI Quality Assurance solution from Visor.ai. Powered by cutting-edge artificial intelligence, this innovative solution automates the entire call auditing process, enabling you to evaluate every single Call Center interaction based on your specific criteria — all with unparalleled efficiency.

With AI Quality Assurance, every call can be thoroughly analyzed, ensuring a comprehensive and unbiased performance evaluation of all agent interactions. It’s all done with precision and consistency. Say goodbye to manual evaluations and embrace an automated solution that empowers you to enhance Call Center performance and deliver superior customer experiences.

AI Quality Assurance

AI Quality Assurance is a groundbreaking tool for automating the Call Center audit process. Designed to be flexible and modular, it can be easily customized through an intuitive, user-friendly interface, requiring no technical expertise. At its core, this innovative tool leverages advanced Speech-to-Text (STT) models and cutting-edge Large Language Models (LLM) to ensure agent performance evaluations are conducted with exceptional accuracy.

In addition to conducting detailed agent performance evaluations, AI Quality Assurance can be customized to extract valuable business insights from each call, such as call summarization, topic classification, and even sentiment analysis. These insights offer a deeper understanding of the overall Call Center experience and can then be used to enhance Call Center performance and strengthen customer relationships.

The technology’s ability to process long audio files in minutes and handle large volumes of data streamlines the entire auditing process. With this solution, Call Center evaluators can quickly access agent performance evaluation for recent calls, significantly reducing the workload of manual evaluation. This allows evaluators to focus on higher-value tasks, ultimately boosting productivity and improving service quality.

Conclusion

AI Quality Assurance from Visor.ai is not just a tool — it’s a strategic asset for any Call Center operations. By automating agent performance evaluations and providing rich, actionable insights, it enables you to maintain consistent quality, optimize agent productivity, and elevate the overall customer experience. Move beyond manual audits and unlock the full potential of Visor.ai technology. To learn more about this feature and how it can enhance your business, contact our team.

João Granja
Senior Data Scientist

Articles

Gen.AI Uploader: Enhancing LLMs with RAG for Accurate AI Solutions

Large Language Models (LLMs) have revolutionized the AI landscape, pushing the boundaries of what’s possible by generating human-like content with incredible precision and quality. However, while these models have shown impressive capabilities, there are some challenges when maintaining up-to-date knowledge and handling domain-specific topics.

The Power and Limitations of LLMs

LLMs are pre-trained on large text datasets, allowing them to learn the intricacies of linguistics, such as syntax and semantics.

Despite this impressive training, LLMs often face limitations due to the broad nature of their knowledge base, which can become outdated in the fast-moving world of today. When it comes to complex and specialized domains, LLMs can produce “hallucinations” — false, inaccurate, or nonfactual information that might seem plausible but lacks authenticity.

The Role of Retrieval-Augmented Generation (RAG)

To overcome these limitations and enhance the performance of LLMs, a solution called Retrieval-Augmented Generation (RAG) has emerged. This technique improves LLMs' ability to generate accurate and contextually relevant information by integrating external sources of knowledge.

RAG empowers LLMs to access up-to-date information, providing a mechanism to generate responses that are not only human-like but also aligned with the latest facts and specific to the domain in question.

Visor.ai’s Gen.AI Uploader

Visor.ai has leveraged the power of RAG by introducing the GenAI Uploader, a user-friendly feature that enables our clients to enhance the performance of LLMs in their specific industries. By allowing users to upload important external documents such as PDFs, URLs, or DOCX files, the GenAI Uploader integrates these tailored datasets with the generative power of LLMs.

With GenAI Uploader, our clients unlock the power to customize RAG's behavior and responses, minimizing “hallucinations” and ensuring accurate interactions. What sets Visor.ai apart is our ability to make use of LLMs and RAG performances to deliver a fully tailored and trustworthy answer.

How Does It Work?

With an intuitive dashboard, clients can easily upload domain-specific content to ensure the AI uses relevant and current information. This integration helps avoid the pitfalls of outdated or generalized knowledge and ensures that the AI can address more complex, specialized topics with greater precision.

Conclusion

The Gen.AI Uploader represents a leap forward in AI technology, combining the extensive capabilities of LLMs with the power of the Retrieval-Augmented Generation. By empowering users to input specific, up-to-date knowledge, Visor.ai provides a tailored, high-performance solution that helps businesses solve complex problems with confidence and accuracy. Contact our team and discover how Gen.AI Uploader can transform your business.

Articles

H.ermes: AI-Powered Language Mastery

Artificial Intelligence (AI) technology is advancing at an unprecedented pace, largely driven by the rapid development and public release of new Large Language Models (LLMs). While these models offer impressive performance improvements, transitioning to newer versions can pose challenges due to variations in their output and behavior. These variations can often lead to unwanted and unexpected responses, which can negatively impact services. Ensuring the stability and quality of services during model transitions can be both time-consuming and technically demanding. To address these challenges, Visor.ai now offers two new multi-modal approaches: H.ermes and H.ermes 2!

What is H.ermes?

H.ermes and H.ermes 2 are multi-modal approaches that use state-of-the-art Large Language Models, specifically generative pre-trained transformers (GPT), developed internally by Visor.ai, along with classic machine learning models.

A multi-modal approach leverages the advantages of both deep learning and traditional machine learning, leading to potentially superior performance across a wide range of applications. Compared with classic machine learning techniques, the H.ermes multi-modal approach provides deeper reasoning and understanding of complex contexts, variability and ambiguity present in the human language. It can handle tasks such as summarization, text generation, and question answering. Each task can be customized through an intuitive, user-friendly interface, requiring no technical expertise. 

On the other hand, compared with LLMs alone, multi-modal approaches benefit from the precision and accuracy that traditional machine learning models can offer for specific, narrow tasks.

Conclusion

These multi-modal approaches are designed to be continuously updated to keep pace with advancements in AI technology while maintaining the stability and quality of service for our clients. This is achieved by employing a variety of AI techniques to reduce AI hallucinations - a phenomenon where the model generates information that sounds plausible but is factually incorrect, misleading, or entirely fabricated. For clients who may require a smaller LLM model at reduced costs, Visor.ai offers H.ermes. Conversely, for clients needing a more powerful LLM model, Visor.ai provides H.ermes 2. Another advantage compared to other LLMs is the continuous client support provided by our teams.

Articles

How Generative AI is Transforming Customer Service Dynamics

In recent years, the incorporation of artificial intelligence (AI) into different industries has changed the way companies operate and engage with their customers. Generative AI is a major development in this field, with the potential to revolutionize customer service. In this article, we'll look at what generative AI is, how it affects customer service, the benefits it provides to organizations, examples of its use, and best practices for efficiently deploying it.
Customer service has quickly become a major management focus, even more so since the introduction of artificial intelligence. In fact, 85% of executives believe generative AI will interact directly with customers in the following two years.

What is generative AI?

Generative AI is a form of artificial intelligence system that can generate new content, such as text, pictures, or even entire conversations, by learning patterns from big datasets. Generative AI, unlike traditional rule-based systems, recognizes context and generates contextually relevant responses, resulting in a powerful instrument for natural language processing and understanding.

How does Generative AI affect customer service?

Generative AI has a significant impact on customer service. These systems employ machine learning algorithms to analyze and comprehend customer inquiries, providing intuitive and context-aware responses. This enhances the effectiveness of client connections, reduces response times, and allows companies to expand their customer support operation without increasing human workforce.

The Benefits of AI for Customer Service

Efficiency and Scalability: Generative AI automates routine customer interactions, allowing human agents to focus on more complex and specialized tasks. This results in increased efficiency and the ability to manage a large number of requests.

Reduce operational costs: By automating routine tasks and optimizing processes, generative AI can help companies decrease operational expenses. Therefore, freeing up resources for more strategic initiatives.

Multilingual support: Generative AI can provide assistance in almost any language, removing communication barriers and broadening businesses’ reach to a worldwide audience.

Consistent and reliable Responses: Generative AI delivers accurate responses, lowering the risk of human error and providing customers with reliable information.

Data-driven Insights: AI systems can evaluate client interactions, providing valuable insights into customer preferences, issues, and trends, allowing organizations to make informed decisions.

Examples of Generative AI Applications in Customer Service

Virtual Assistants: AI-powered Virtual Assistants, also known as chatbots can interact with customers in real-time, answering questions, offering information, and even assisting in various processes.

Automated Email Responses: Generative AI can be used to create personalized and contextually appropriate email responses, thereby speeding up communication and increasing consumer engagement.

Voice Virtual Assistants: Voice-activated virtual assistants use generative AI to interpret and respond to spoken language, improving customer experience in contact centers and other spoken interactions.

Best Practices for The Implementation of Generative AI in Customer Service

Understand Customer Needs: Before implementing Generative AI, carefully examine your customer’s needs and expectations so that the AI system can be tailored accordingly.

Continuous Learning: Implement solutions that are capable of continually learning from fresh data and adjusting to changing customer needs and patterns, assuring the AI’s effectiveness over time.

Human Oversight: While AI is a powerful tool, it still requires human supervision to handle complex tasks and sensitive information. These tools must be applied in a way that ensures that ethical considerations are met.

Transparent Communication: When exposing your customers to AI-powered interactions, clear expectations must be set. Communicate transparently about the capabilities and limitations of this technology.

Provider selection: If your company isn’t able to internally develop and implement AI-powered solutions, choose carefully between the vast number of providers in the market. AI technology for broad use is still in its early stages and is therefore rapidly evolving. Choose a technological provider that will be able to provide continuous support and necessary adjustments to the solution.

Conclusion

Generative AI is transforming customer service, improving efficiency, scalability, and satisfaction. As technology advances, companies that carefully implement and integrate generative AI into their customer service operation stand to gain a competitive advantage in the market. If you’re still hesitant about implementing AI solutions in your operations, or would simply like to know more about what type of tools are better suited to your specific needs, reach out to our team. Our solutions are entirely customizable and our team provides continuous support. We’re committed to providing support not only during the implementation process but throughout your entire journey with us.