DeepSeek large model ignites AI market, enterprise knowledge base becomes key implementation scenario

The popularity of DeepSeek's large model has made generative AI the focus of the market again. After the craze, my team and I have been thinking about a core question:

In addition to content creation, programming and other scenarios, in what other fields can AI be truly implemented?

After in-depth research, we came to the following key conclusions:

1. Enterprise knowledge base products are the most competitive application scenarios for generative AI . Whether it is intelligent customer service for customers or knowledge questions and answers for employees, AI can significantly improve communication efficiency.

2. RAG (Retrieval-Augmented Generation) technology is the core driving force for AI to empower enterprise knowledge bases . In the future, RAG technology will be more decoupled and modularized, helping enterprises achieve flexible knowledge management and question-answering capabilities.

3. Industry scenarios are deepening . The demand for AI in the financial, legal, and medical industries is particularly strong, and RAG technology will gradually penetrate into these fields.

Industry status: Knowledge assistants and intelligent customer service dominate

In the application scenarios of generative AI, enterprise knowledge base has become the core implementation direction. From the data:

• Knowledge assistants and intelligent customer service  account for the largest proportion, and are mainly used for customer communication and internal support.

• Data analysis and office assistants  followed closely behind, helping companies optimize decision-making efficiency.

In terms of industry demand, banking, securities, and automobile manufacturing are particularly prominent. These fields have a large amount of unstructured data, and AI can help companies efficiently manage knowledge assets and provide accurate questions, answers, and analysis.

The evolution direction of RAG technology

With the development of RAG technology, it is gradually evolving from basic applications to more advanced intelligence.

1. Paradigm evolution: from Naive RAG to Modular RAG

Early RAG systems mostly used Naive RAG , which simply connected the retrieval and generation modules in series to perform extensive information recall.

The current Modular RAG is more flexible, decoupling modules such as retrieval, generation, re-ranking, and reasoning. Enterprises can freely combine different modules according to scenario requirements to optimize the quality of results.

For example:

• In customer service scenarios, a more powerful search optimization module is used to improve the response speed to user questions.

• In the legal or medical fields, reasoning modules are introduced to ensure that the generated answers are professional and accurate.

  
  

2. Technology evolution: Agent + multimodal + hybrid retrieval

With the development of AI technology, RAG's capabilities are also expanding:

• Agentic RAG : Through the AI ​​Agent approach, it combines retrieval, generation, logical reasoning and other capabilities to achieve more complex tasks.

• Multimodal RAG : Not only supports text, but also can process multiple data types such as images, audio, video, etc., further broadening the application scenarios.

• Hybrid retrieval : Combines full-text retrieval, vector retrieval, knowledge graph and other technologies to ensure the accuracy and comprehensiveness of information recall.

This diversified technological evolution will greatly enhance the intelligence level of corporate knowledge bases.

3. Deepening of industry applications: exploration of scenarios such as finance, medical care, and law

In practical applications, RAG technology is gradually penetrating into some highly professional industries:

• Financial industry : Help investment analysts quickly access market data and research reports to improve investment decision-making efficiency.

• Medical industry : Provide medical literature search and auxiliary diagnosis, and provide doctors with accurate reference information.

• Legal industry : Support lawyers to search for legal provisions and related cases to improve case handling efficiency.

In these scenarios, AI is not only an information retrieval tool, but also an important assistant for corporate decision-making.

Enterprise Knowledge Base: Core Product Types and Market Status

in the country, the product forms of enterprise knowledge bases are gradually enriched and can be roughly divided into the following three categories:

1. Customer service robots: From rule-driven to intelligent upgrade

Customer service robots, represented by  Alibaba Cloud Tongyi Xiaomi  , are one of the most mature application scenarios of enterprise knowledge bases.

These products are usually upgraded from traditional customer service systems and have the following characteristics:

• Industry solution accumulation : Provide industry FAQ knowledge packages and customer service scenario templates to significantly reduce the company's construction costs.

• Intelligent question-and-answer capabilities : Supports multiple scenarios such as multi-round dialogues, task-based conversations, and knowledge graph question-and-answer sessions.

• Knowledge base management tool : It has the capabilities of sensitive word detection, data analysis, knowledge clustering, etc., to help companies continuously optimize customer service efficiency.

However, at this stage, customer service robots are still mainly based on traditional rules + large models , and there is still a certain gap from the pure AI Native form.

2. Agent-based question-answering assistant: an explorer of AI Native

Unlike traditional customer service robots, products such as Monster Intelligence AI Knowledge Base and EasyLink are typical AI Native solutions.

With intelligent search and deep understanding as the core, they directly extract knowledge from the company's documents, pictures, videos and other data, and have the following advantages:

• Flexible business orchestration : Supports Agent to perform task decomposition and dynamic execution to meet the needs of complex business scenarios.

• Fusion of multiple data sources : connect internal and external data of the enterprise to achieve unified management and efficient retrieval.

• Intelligent analytical capabilities : not only provide answers, but also combine data for insights and analysis.

  
  

For enterprises that need to efficiently acquire knowledge and make real-time decisions, agent-based question-answering assistants will become an important productivity tool.

3. Enterprise internal knowledge management: data security and efficient collaboration

In internal enterprise scenarios, products such as DeepBlueFish and  Askbot focus on the accumulation, management, and sharing of enterprise knowledge.  

These products usually have the following characteristics:

• Private deployment : Ensure enterprise data security and meet compliance requirements.

• Multi-dimensional knowledge management : supports tags, hierarchical structures and permission management to facilitate orderly storage and retrieval of knowledge.

• Collaboration support : Combined with AI to provide intelligent recommendations, knowledge supplementation and other functions to improve team collaboration efficiency.

When the enterprise is large in scale, internal knowledge management tools become an important platform for knowledge reuse and experience accumulation.

Open Source Knowledge Base

Open source RAG projects represented by  MaxKB , dify , FastGPT , RAGFlow , and QAnything each have their own advantages: 

• QAnything emphasizes the Rerank mechanism and improves document recall quality through the combined use of Embedding + Rerank models

• RAGFlow focuses on optimizing the refined parsing of documents, emphasizing high-quality input and high-quality output.

• Dify provides a variety of recall modes, cross-knowledge base recall capabilities, QA modes, and workflow orchestration functions.

• FastGPT has good scalability and task flow orchestration capabilities. The commercial version has rich functionalities and high accuracy.

There is still a long way to go for the basic capabilities of the open source knowledge base project to be truly commercialized, but overall the basic capabilities are already there, and the core needs to be optimized and iterated by going deep into specific industry cases. Regarding the open source knowledge base, I will give a special topic for horizontal evaluation later.

Summary: Enterprise knowledge base is at a critical stage of evolution

The domestic enterprise knowledge base product ecosystem can be divided into two types:

1. Traditional knowledge base & intelligent customer service : gradually upgraded by adding large model capabilities.

2. AI Native solution : built directly on RAG and Agent, with higher flexibility, but still in the early stages of the market.

In the future, the evolution direction of enterprise knowledge base includes:

✅Data  security and compliance——  Ensure enterprise data privacy and support private deployment.

✅High  accuracy——  Combining hybrid retrieval and intelligent reasoning to improve the accuracy of AI answers.

✅Multimodal  support——  Processing multiple data types such as text, images, audio, etc.

✅In  -depth industry application——  Combined with specific industry needs, to create highly adaptable AI solutions.

In the AI ​​era, the enterprise knowledge base is not only a storage and retrieval tool, but also an important engine for improving enterprise efficiency.