Cloopen AI

Model value: business problem-solving, not parameter accumulation

Based on this, cloopen Cloud has upgraded its new large-scale model application, Knowledge Assistant, which is deeply adapted based on the DeepSeek R1


Based on this, cloopen has upgraded its new large-scale model application, "Knowledge Assistant", which is deeply adapted based on the DeepSeek R1 large model. With three core capabilities of enterprise-level AI search, scenario-based knowledge questioning, and private deployment, it provides intelligent knowledge solutions for enterprises, making cutting-edge AI technology truly become the "production engine" of the enterprises. 

 

The core highlights include:

  • Enterprise-level AI search: Utilizing semantic understanding capabilities, it enables in-depth analysis and intelligent retrieval of complex documents, supporting multi-dimensional search requirements and enhancing search efficiency.
  • Knowledge base AI assistant: Creating a scenario-based AI question-answering engine, adaptable to multiple roles, ensuring the timeliness and authority of answers, and avoiding illusion risks.
  • Private DeepSeek service: Local deployment ensures data security, supports seamless integration with existing systems, and enhances the intelligence of business processes. 
  • The application scenarios in the financial industry include:
  • Intelligent customer service: Dynamic knowledge integration enhances consultation handling efficiency and reduces the rate of manual transfers.
  • Credit risk control: Establishing a private knowledge base improves the efficiency of credit inquiries and the accuracy of risk warnings.
  • Compliance management: Creating a dynamic policy library shortens the response time for policy updates and enhances the efficiency of compliance audits.

 

The core value is as follows:

  • Deep internalization of industry Know-How: Specialized model tuning to solve professional problems, breaking through the specific problems of enterprise private domains.
  • Capabilities for large and medium-sized enterprise deployment: Supporting high-concurrency architecture, adapting to various architectures, and improving the utilization rate of knowledge and the efficiency of employee training.
  • Secure and trustworthy AI new infrastructure: Establishing a triple protection system, supporting manual correction and feedback, and enabling continuous optimization of the model.

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