Generate Insight

The Searching feature in no-code platforms enables natural language queries, allowing users to ask questions and get instant AI-powered responses by fetching real-time data from databases, offering context-aware, secure, and automated insights.

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Real-time Database Query

The AI searching is deeply integrated with the backend database, enabling intelligent correlation of multidimensional business data, including application records, member profiles, and voting results. It provides users with a truly dynamic and real-time data interaction experience. This eliminates the inefficiency of traditional manual queries and supports cross-table querying.

Condition Filtering

The AI searching offers an intuitive complex condition search feature, allowing simultaneous combination of multiple filtering criteria such as “experience+expertise+location.” It can instantly generates structured analysis reports for applicants and automatically tallies the background distribution of various applicants, transforming complex data into lists, visual charts, and summaries of key metrics. This helps managers quickly grasp the overall picture of the filtering results.

Loyalty management scenario

When the case manager queries: “The Members eligible for this month’s coupon redemption.”

The system can cross-analyze membership and transaction data to identify members with a points balance exceeding 10,000 and expiring within 30 days, and generate a list of members that includes their contact information.

Intelligent Recommendation

Utilizing advanced matching algorithms, the AI searching conducts in-depth analysis against preset criteria and the applicant database to automatically identify and highlight top candidates/projects with the greatest potential for development. It also employs machine learning models to intelligently recommend “similar candidates,” significantly expanding the selection scope and precision. Additionally, it provides visual matching scores and comparative analyses of key capabilities.

Predictive Insights

Leveraging advanced machine learning models, AI searching analyzes historical data and candidate characteristics to establish an accurate success rate prediction system.

In the voting scene, our model can intelligently assesses the application, automatically flags high-potential candidates, and provides intuitive decision-making scores, helping teams quickly identify the most likely successful candidates/projects.

Intelligent Context Understanding

The system has intelligent context understanding and multi-turn dialogue capabilities with deep context memory. It automatically retains key parameters from conversation history, such as time ranges, filtering criteria, and user identity, and supports cross-turn parameter references. Additionally, it dynamically adapts permissions based on user roles, filtering accessible data and adjusting the level of detail in responses.

Deep Context Memory

Automatically retains key parameters from conversation history (such as time ranges, filtering conditions, and user identity), and supports parameter references across dialogue turns.

Award Management Scenario

User: “Show me last month’s award winners.”
AI: “In October 2023, a total of 12 awards were granted, primarily in the fields of Innovation Technology (5 awards), Sustainable Development (4 awards), and Social Impact (3 awards). Would you like to see the detailed list?”
User: “Just the Innovation Technology ones.”
AI: “I have filtered out the 5 winners in Innovation Technology: 1. Company A, 2. Lab B… Would you like to export their contact information?”

Dynamic Permission Adaptation

Integrates with the corporate RBAC (Role-Based Access Control) system to achieve dynamic data filtering. Automatically adjusts the level of detail in responses based on user roles to ensure data security. For example, administrators receive full reports, while regular employees only see summaries.