Client Cases
See How Other Enterprises Use AI to Boost Efficiency
Real business problems and measurable results showcase the actual value of AI in enterprises.
R&D Enterprises / Labs
Materials R&D AI System
Literature retrieval, experiment planning, result analysis, and knowledge accumulation — previously done step by step manually, now completed automatically by AI.
Business Challenge
Problems the Client Faced
Literature retrieval, experiment planning, result analysis, and knowledge accumulation — previously done step by step manually, now completed automatically by AI.
System Integration
◎AI Agent Execution Workflow
Literature retrieval
Experiment planning
Analysis & attribution
Report generation
◆Business Results
R&D effective iteration speed improved
Knowledge accumulation efficiency increased
Sensitive data security boundaries clearer
Core Capabilities
Multi-Step Auto Execution
AI automatically completes the full workflow from search to analysis to planning to reporting — no manual step-by-step operation needed.
Business Capability Encapsulation
Enterprise business rules and system interfaces are encapsulated into standardized capabilities for AI to use.
Continuous Knowledge Retention
Experiment data and R&D experience auto-accumulate — team knowledge assets won't be lost due to personnel changes.
Enterprise-Grade Security
Data access control, operation approval, audit logging — meeting enterprise compliance requirements.
More Cases Coming Soon
Supply Chain Procurement Collaboration
Solving procurement challenges: price volatility monitoring gaps, slow supplier coordination, and time-consuming compliance audits.
Manufacturing Quality Closed-Loop
Quality issues automatically progressed from discovery to resolution, reducing closed-loop time and improving cross-department collaboration.
Trade & Distribution Procurement Automation
Procurement, replenishment, fulfillment, and reconciliation automated — faster anomaly response, improved supply chain transparency.
Chemical R&D Knowledge Management
Experiment data and process experience automatically accumulated — improved R&D iteration efficiency, knowledge retained despite personnel changes.
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