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Scenario Cases

Scenario 1: Natural Language Q&A for Data​

Typical Application: Rapid querying and response for complex business data

Customer Case: Sales Data Query for a Consumer Goods Company

The management of this company often needs to review sales performance across regions and products. Previously, they relied on data analysts to manually generate reports, which was inefficient and slow to respond.

After introducing the AI data Q&A assistant, users can directly ask:

  • “What was the sales revenue of personal care products in East China in Q1 2025?”
  • “Which category grew the fastest compared to the same period last year?”

The system automatically parses the questions, queries the data in real time, and generates natural language answers, sometimes accompanied by trend charts or brief analyses.

Effectiveness Improvement:

  • Query time reduced from hours to seconds;
  • Managers can ask questions independently without relying on analysts;
  • Covers multi-dimensional data (time, region, category, etc.) with flexible usage.

This solution helps the company significantly improve data acquisition efficiency and decision response speed.

Scenario 2: Product Performance Comparison and Recommendation​

Typical Application: Quickly finding the option that best meets needs among multiple products

Customer Case: Intelligent Product Selection Assistant for an E-commerce Platform

An e-commerce platform launched an AI product recommendation assistant to help users efficiently shop among a vast number of products. Users only need to input natural language descriptions, such as:

  • “I want to buy a camera with clear shooting quality and a budget around 8000”
  • “Are there any lightweight laptops recommended for outdoor sports?”

The system automatically analyzes user needs, filters matching items from the product database, and recommends 2-3 preferred products, with comparison parameters, performance highlights, and usage scenario explanations.

Effectiveness Improvement:

  • Improves user shopping efficiency, shortening decision time by an average of 60%;
  • Recommendations better match real needs, significantly increasing conversion rates;
  • Continuously learns user preferences to optimize recommendation results.

This feature is widely applicable to personalized recommendation needs in e-commerce, offline retail, 3C digital products, and other fields.