Large Model Privatization
Customize Exclusive Intelligent Brain
Large model privatization refers to deploying advanced large language models to enterprise internal servers or private cloud environments, where enterprises can fully control the model's operation, data processing, and optimization processes, ensuring data security and highly customized business needs. At the same time, enterprises can build their own knowledge bases or train privatized proprietary models to make the privatized large models more aligned with their own business scenarios, thereby achieving precise decision-making, efficient operations, and innovative services, building unique advantages for enterprises in digital competition.
Knowledge Base Building
Knowledge Base Building
A high-quality knowledge base is the cornerstone of efficient operation of large models. The core of knowledge base construction is through the closed loop of "collection → governance → organization → update → collaboration", transforming fragmented enterprise data, industry knowledge, historical cases, and experience into structured knowledge that models can understand and utilize. This not only provides the model with training materials covering all business scenarios, but also endows the model with reasoning capabilities through technologies such as knowledge graphs and dynamic retrieval, enabling it to make more intelligent and explainable decisions based on comprehensive information, ultimately achieving the upgrade from "data-driven business" to "knowledge-driven intelligence".
Private Exclusive Model Training
Build Private LLM
Business Pain Point Analysis + Goal Alignment
Ensure the model solves practical problems
Basic Model Screening（such as LLaMA/Alpaca variants）
Architecture Adjustment+Parameter Optimization（adapted to business scenarios）
Annotation: Adding Labels → Supervisory Signals
Training: Large-scale Data Parameter Tuning → Enhancing Business Understanding
The Benefits of Privatization
The Benefits of Private LLM
Emphasizes that during the model training process, all data is processed in the enterprise's private environment, strictly complying with data security regulations, ensuring that enterprise data is not leaked, and protecting the enterprise's core competitiveness.
Compared to general models, privatized exclusive models are trained on enterprise-specific business data, enabling more precise understanding and execution of enterprise tasks. For example, in a risk assessment scenario at a financial enterprise, the model's accuracy improved by 72 percentage points compared to general models, providing more reliable support for enterprise decision-making.
Success Cases
Success Cases
Brand Name
Technology Network
Emphasize that during the model training process, all data is processed within the enterprise's private environment, strictly adhering to data security regulations, ensuring that enterprise data is not leaked, and protecting the enterprise's core competitiveness.
75%
Cost reduction
75%
Efficiency improvement
95%
Customer satisfaction has improved
Brand Name
Technology Network
Emphasize that during the model training process, all data is processed within the enterprise's private environment, strictly adhering to data security regulations, ensuring that enterprise data is not leaked, and protecting the enterprise's core competitiveness.
75%
Cost reduction
75%
Efficiency improvement
95%
Customer satisfaction has improved
Email: tomy.tang@fusionprism.com
