Enterprise Knowledge Base + Vertical LLM Solution
Beta Enterprise Knowledge LLM
Build enterprise internal knowledge brain, combining RAG technology and domain fine-tuning to provide professional and reliable intelligent Q&A services.
Background
Enterprise internal knowledge is scattered and highly specialized, generic LLMs struggle to accurately understand industry content.
Data Security
Industries emphasizing data security are not suitable for uploading knowledge to external platforms for training and Q&A.
High Data Annotation Costs
Vertical industries lack large-scale annotated data, manual annotation is costly and time-consuming, difficult to meet model training requirements.
Solution Content
Building 'enterprise internal knowledge brain' in three steps:
Knowledge Collection
Connect to OA, document library, ERP, CRM, email systems, complete parsing, vectorization and metadata annotation, making unstructured data searchable and relatable.
Vertical Model
Perform domain fine-tuning on private platform, combine RAG technology to inference and call latest knowledge base content, ensuring professional and reliable answers.
Application Integration
Provides unified entry, supports web, mobile and office IM bot access, traceable citation sources for verification.
System deployed on enterprise intranet, accelerated by proprietary computing, achieving high concurrency and low latency.
Key Advantages
Let data speak — let results be visible
Accuracy Improvement
Vertical domain Q&A accuracy reaches 90%+
Data Leakage
Fully intranet deployed, zero data leak risk
Efficiency Boost
Knowledge retrieval efficiency improved by 80%+
Smart Service
24/7 intelligent Q&A service
Technical Capabilities
Deep technical capabilities powering your business
RAG + Fine-tuning Hybrid
Combines Retrieval-Augmented Generation (RAG) with domain fine-tuning, ensuring real-time answers while improving professional terminology understanding with traceable knowledge sources
Multi-source Knowledge Management
Supports integration with OA, document libraries, ERP, CRM, email and other data sources, automatically parsing, vectorizing and annotating metadata for unified knowledge retrieval
Private Secure Deployment
Complete system deployed in enterprise intranet, data stays within network, meeting compliance requirements for highly regulated industries like finance, healthcare, and government
Dynamic Knowledge Updates
Supports real-time incremental knowledge base updates, obtaining latest information without retraining models, reducing maintenance costs and ensuring knowledge timeliness
Target Industries
This solution is designed for the following industries and use cases
Finance
Legal
Manufacturing
Healthcare
Education
Government
Enterprises
Implementation Process
Expert-guided implementation ensuring seamless project delivery
Knowledge Inventory
Review enterprise knowledge sources and scenarios
Data Integration
Connect systems, complete knowledge parsing and vectorization
Model Training
Domain fine-tuning and RAG configuration optimization
Application Integration
Develop web, mobile, and IM bot interfaces
Testing & Launch
Gray release, staff training, and official launch
FAQ
Common questions answered
Traditional fine-tuning trains knowledge directly into model parameters - fast response but costly to update. RAG (Retrieval-Augmented Generation) dynamically retrieves from knowledge base during inference - real-time updates and traceable but slightly slower initial response. Our solution combines both: domain fine-tuning for terminology understanding plus RAG for latest knowledge, ensuring both professionalism and timeliness.
We use vector database + incremental update mechanism. New or modified documents automatically trigger parsing, vectorization and index updates (usually 5-30 minutes), no model retraining needed. System supports scheduled sync and event-driven modes configurable to business needs. For urgent updates, manual immediate refresh is supported. Additionally, system records knowledge versions, supporting historical answer retrieval for audit traceability.
We provide multi-dimensional evaluation: 1) Accuracy assessment: build domain standard Q&A test sets, calculate accuracy, recall and F1 scores; 2) Professional assessment: business experts rate answer professionalism, completeness and compliance; 3) User satisfaction: track likes/dislikes, rewrite rates and feedback metrics; 4) Efficiency metrics: average response time, knowledge coverage and resolution rates. System includes built-in evaluation dashboard supporting A/B testing to compare different models or configurations.
Recommended basic configuration: 1) Computing: 4-8 GPU cards (A100/H100 or domestic Ascend 910) for inference, 4 cards support ~5000 daily queries; 2) Storage: Vector database needs SSD, 1M documents require ~500GB-1TB; raw document storage separate; 3) Memory: 256GB+ for inference nodes, 128GB+ for vector search nodes. We support on-demand scaling, starting small (2 cards) and expanding gradually. For SMEs, hybrid deployment is available: cloud GPU for inference, local knowledge base.
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