You may have experienced this: when chatting with AI, it can help you write poems, generate code, or even explain complex concepts like a teacher — its intelligence can be astonishing. But when you ask it for your company’s latest sales data, or to book a meeting room, it suddenly seems to “forget” everything and cannot respond. Why does this happen?

The reason is that most AI models do not directly access your proprietary information; they mostly rely on general knowledge learned from training data. To enable AI to truly “get things done,” it requires two key components: Agents and Knowledge Bases. The agent serves as AI’s “action brain,” capable of understanding instructions, invoking tools, and executing tasks; the knowledge base acts as AI’s “memory repository,” storing enterprise data, documents, and rules so the AI can access and utilize up-to-date information.

We actually interact with AI every day. Opening ChatGPT to draft a copy? That’s AI. Using your phone to take a photo of a dish and get calorie information? That’s also AI. Translation apps, speech-to-text, automatic recognition in your photo library—all rely on AI.

They may look similar, but the underlying mechanisms are completely different.

• General-Purpose Model: Can handle a wide range of tasks. ChatGPT is an example. In academia, you might also hear the term Foundation Model, while in practical use, people often refer to Large Language Model (LLM).
• Inference Model: Specialized for a specific type of task, such as image recognition, translation, or speech recognition. Sometimes called Deployment Model or Task-Specific Model, emphasizing optimization for specific tasks.

Why differentiate? Because it determines how you should use them:

B2B and B2C marketing both require effectiveness measurement, but their focus differs. B2C marketing typically targets individual consumers, with shorter decision chains and higher purchase frequency. Therefore, B2C metrics often emphasize immediate conversions, user engagement, and customer lifetime value. B2B marketing, on the other hand, targets business clients, with more complex purchasing decisions, longer cycles, and multiple decision-makers. As a result, B2B marketing focuses more on lead quality, sales conversion rates, and the contribution of marketing to the entire sales funnel.

Why Should We Care About Content?

In today’s digital world, we interact with “content” almost every day. Whether browsing websites, scrolling through short videos, using mobile apps, or chatting with smart assistants, the core of these experiences is various forms of content. In other words, content has become the primary medium through which we communicate, gather information, and make decisions.

However, despite being ubiquitous, content often faces a common problem: fragmentation and duplication.

What Is an Ad Network?

In the internet advertising ecosystem, an ad network plays a crucial but often overlooked role. Simply put, it acts like a middleman, connecting advertisers and publishers, enabling ads to circulate efficiently across websites, apps, and video platforms.

Without ad networks, advertisers would need to negotiate with each media individually, which is very inefficient. At the same time, small and medium-sized websites or apps would struggle to connect with enough advertisers, often leaving ad space unsold. Ad networks solve this “two-sided problem.”

In today’s highly competitive business environment, the question of “how to make money” is no longer the only challenge. The real question is: how to find a sustainable monetization path that fits your business.

Different products and companies face varying user groups, product types, and market environments. For some businesses, leveraging large-scale traffic for advertising is a shortcut. Others rely on subscriptions or value-added services to generate revenue. Some companies adopt a hybrid model to balance short-term gains with long-term value.