Since the emergence of large language models (LLMs), we’ve witnessed their astonishing capabilities in text generation, summarization, and Q&A. However, no matter how intelligent these models are, they mostly remain at the “chatbot” stage—passively responding to queries, relying on static training data, and lacking the ability to interact with the real world. Imagine telling an AI:“Check our CRM system for last month’s top-performing customer and automatically send them a thank-you email.” If the AI replies:“I’m sorry, I don’t have access to your CRM system or email tools,” its usefulness would be greatly diminished. To bridge this gap between AI and real-world action, the tech community introduced a groundbreaking open standard — the Model Context Protocol (MCP). MCP effectively gives LLMs “hands and feet,” transforming them from knowledge conveyors into proactive, task-executing “AI Agents. ”

As AI penetrates across industries, architectural choices are no longer a simple opposition between cloud and on-premise deployments. Instead, they represent a multidimensional trade-off involving performance, latency, privacy, maintainability, cost, and scalability. For technical architects, this is not merely a deployment strategy—it is a long-term business decision.

In 2010, Google acquired a company called Global IP Solutions (GIPS). The core technology of this company focused on real-time voice and video communication, including advanced voice codecs (such as Opus), echo cancellation, noise reduction, and other features.

In the past, telemarketing often carried a negative image of “mass dialing and aggressive selling.” Marketers would hold a list and call each number one by one, hoping to find someone willing to listen amid a sea of rejections. However, this approach was not only inefficient and labor-intensive, but also prone to irritating potential customers. With the advancement of artificial intelligence (AI) and big data technologies, telemarketing has evolved beyond mindless dialing. It has transformed into a strategic and predictive form of intelligent marketing. One of the most representative technologies driving this change is predictive dialing.

In today's digital age, customer service recordings are no longer just simple file backups. With the rapid advancement of artificial intelligence (AI), these seemingly mundane conversation logs are gradually transforming into valuable sources of business insights. Empowered by AI, customer service recordings not only help enhance operational efficiency but also enable deeper understanding of customer needs. They provide critical input for business decision-making, truly allowing companies to "hear the value."

Modern language models—such as ChatGPT, Gemini, Claude, and others—are now widely used across various fields, including customer service, automatic summarization, legal consultation, medical assistance, and code generation. However, to achieve optimal performance in specific scenarios, relying solely on the "base model" is often insufficient. Instead, further adjustment or integration is usually required.

Amid the wave of artificial intelligence, large language models (LLMs) have emerged like mushrooms after the rain, demonstrating impressive abilities in text generation, conversation, and comprehension. However, despite learning from vast amounts of textual data during training, these models also reveal some inherent limitations — such as a lack of knowledge beyond their training data, a tendency to produce "hallucinations" (i.e., generating false or unfounded content), and difficulty responding to the most up-to-date information. To address these challenges, a technique called Retrieval-Augmented Generation (RAG) has emerged. It functions like an "external brain" that AI can consult at any time, significantly enhancing the performance and application potential of generative models.

Artificial intelligence has gradually become deeply integrated into our daily lives, and one influential technology in this realm is the Large Language Model (LLM). The recent popularity of ChatGPT is an application of this technology.

Emotion analysis, also known as sentiment analysis, is a data analysis technique applied to various data types such as text, speech, and images. It encompasses Natural Language Processing (NLP), statistical techniques, and machine learning.

WFO (Workforce Optimization) is typically applied in customer service and call centers. Its main objective is to ensure that an organization's workforce is utilized to its maximum potential at the right time, optimizing human resources and improving overall team quality and efficiency. WFM (Workforce Management) focuses on efficient scheduling and managing the workforce, ensuring that each employee works in the right place at the right time. As the industry evolves, Gartner believes that a people-centric approach to workforce handling is essential, recognizing that optimizing workforce engagement cannot be as effective as it used to be if treated merely as a machine-like optimization process. Therefore, Gartner has transitioned the evaluation category from WFO (Workforce Optimization) to WEM (Workforce Engagement Management).