Xrova - Comprehensive AIGC Application Platform
Xrova is a comprehensive AIGC platform deeply integrated into business operations. It aims to enhance enterprise efficiency, simplify data management, automate processes, and drive innovation by integrating large language models (LLMs), low-code orchestration, and intelligent workflows. The platform supports flexible integration with public and private models, enabling rapid development of customized AI solutions to meet multi-scenario business needs, covering the entire process from intelligent dialogue and knowledge management to process automation.
Product Advantages
- Powerful Model Integration Capability: Compatible with mainstream public and private models such as Azure OpenAI, Wenxin Yiyan, and ChatGLM. It supports model combination and evaluation to accurately match business scenarios.
- Low-Code Efficient Orchestration: Custom AI workflows can be created without complex coding, enabling rapid deployment of intelligent assistants, digital humans, and other applications, reducing development thresholds.
- Out-of-the-Box Application Components: Built-in with enterprise-level chat, knowledge Q&A, ChatBI, and other functions, supporting multi-language interaction and permission control, which can improve team efficiency immediately after deployment.
- Full-Link Intelligent Management: Covers knowledge slicing, vector database management, user feedback tracking, and model monitoring to ensure continuous optimization of AI applications.
Application Scenarios
1
nternal Business Efficiency Improvement
Provide intelligent assistants for sales, procurement, R&D, and other departments (such as sales follow-up and production scheduling), automating report generation and data query.
2
Management Decision Support
Realize data visualization analysis through ChatBI to provide management with real-time business insights and decision-making suggestions.
3
External Collaboration Optimization
Provide intelligent customer service and supply chain collaboration assistants for external roles such as customers and suppliers to improve interaction efficiency.
4
Industry-Specific Solutions
Adapt to vertical scenarios such as intelligent manufacturing (equipment maintenance scheduling), intelligent finance (automatic reconciliation), and intelligent logistics (scheduling optimization).
Case Analysis
Case Enterprise
A large energy manufacturing groupBackground Slow response to equipment failures and long downtime affected production efficiency.
Background Slow response to equipment failures and long downtime affected production efficiency.
ResultsFault response time was reduced by 50% (from 2 hours to within 1 hour), average downtime was decreased by 30%, annual maintenance costs were reduced by 20%-25%, and the overall efficiency of the production line was increased by more than 20%.
