Scenario Cases
Scenario 1: Business Data Analysis
Typical Application: Integrate with business system data for intelligent analysis and insights
Function Description:
The system can connect with existing enterprise business systems to extract structured data, which is then automatically analyzed and presented by AI. Supported analysis types include but are not limited to:
- Product sales analysis
- Global sales comparison
- Financial statistical analysis (such as cost structure, profit trends, etc.)
Users can ask questions in natural language, such as:
- "Which product had the highest sales this month?"
- "How are sales performing across different continents?"
- "What is the trend of expense ratio changes in the first half of 2025?"
Effect Improvement:
- Automatically generates charts, conclusions, and insights, saving analysis manpower;
- Flexible multi-dimensional data queries, supporting custom comparisons;
- Helps business teams quickly identify problems and opportunities.
Applicable to data-intensive departments such as sales, operations, and finance.
Scenario 2: Ticket Data Analysis
Typical Application
In enterprise IT operations or customer support processes, a large amount of ticket data is often accumulated, covering various issue types, processing statuses, responders, and processing timeliness information. Traditional data analysis methods rely on manual aggregation and Excel statistics, which are not only inefficient but also difficult to handle massive data and multi-dimensional analysis needs.
Typical use cases include:
- Providing monthly/quarterly ticket processing analysis reports to customers or internal management;
- Identifying high-frequency failure types, recurring issues, and response bottlenecks;
- Analyzing the efficiency and workload distribution of different engineers;
- Real-time monitoring of trends in unresolved or overdue tickets;
- Quickly obtaining key metrics such as total ticket volume, completion rate, and closure cycle.
Function Description:
In this scenario, we build an intelligent agent based on data sources—"Ticket Analytics"—to achieve automatic aggregation, analysis, and visual presentation of ticket data, with the following core functions:
- Connect to database data sources: Integrate structured ticket data tables (such as support_logs), automatically reading the latest data;
- Natural language analysis commands: Users can ask questions in natural language without needing to know any data query syntax;
- Automatically generate statistical reports: Supports generating statistical charts (bar charts, pie charts, line charts) by ticket type, status, time dimension, etc.;
- Custom chart views: Charts support visual editing, allowing chart type changes and field dimension switching;
- Insights and trend analysis: Built-in intelligent insights function automatically discovers anomalies, trends, key changes, etc.;
- View underlying data and SQL: Supports viewing detailed query results and underlying SQL, enhancing data transparency and analysis traceability.
Effect Improvement:
After deploying this ticket analysis Agent, compared to traditional manual statistics methods, significant improvements in efficiency and quality can be achieved:
| Comparison Item | Traditional Method | Ticket Analysis Agent |
|---|---|---|
| Data Processing Efficiency | Manual operation, time-consuming | Automatic response, results in seconds |
| Multi-dimensional Statistics | Complex operation, error-prone | Natural language queries, flexible multi-dimensional statistics |
| Visualization | Relies on manual charting, single style | Automatically generates charts, supports flexible editing |
| Insight Capability | Relies on experience, hard to find potential patterns | Intelligently identifies trends, anomalies, and related issues |
| Report Generation | Multi-person collaboration, long cycle | One-click report generation, fast delivery to customers |
✅ The ticket analysis Agent greatly lowers the data processing threshold for operations engineers, improves the response speed and service quality of support teams, and is an important tool for enhancing digital operations capabilities.
