Blog Insights
Technical Insights and Industry Perspectives
Sharing our practical experience and forward-looking insights in AI, automation, and software development
How to Define AI Project Acceptance Criteria? Functionality, Performance, and Security Are All Essential
AI project acceptance is far more complex than traditional software. This article provides a complete AI project acceptance criteria template covering four dimensions: functionality, performance, security, and effectiveness.
Why 80% of AI Projects Fail to Go Live Smoothly? 7 Delivery Pitfalls and Countermeasures
AI project delivery differs fundamentally from traditional software projects. This article analyzes 7 common pitfalls and coping strategies to help AI projects transition successfully from demo to production.
Core Data Can't Go to the Cloud? Hybrid Cloud AI Architecture Keeps Data Local, Capabilities in the Cloud
An analysis of hybrid cloud AI architecture design principles, including data classification, model layering, and traffic routing, helping enterprises balance security and cost.
How Will AI Automation Evolve from RPA to Agents in 2026?
Traditional RPA can only execute fixed rules; how does next-generation AI automation enable business processes with reasoning capabilities? This article explores the technical evolution path, core architecture design, and enterprise implementation practices of AI automation.
How Enterprises Integrate Large Models into Existing Systems: From API Integration to Business Deployment
A comprehensive guide on selecting and integrating public LLM APIs like Tongyi, DeepSeek, and GPT, and the complete practical path for embedding AI capabilities into ERP/CRM/OA systems.
How to Do Large Model Private Deployment? 7 Steps from Selection to Going Live
A detailed guide on how enterprises select open-source large models, evaluate computing requirements, deploy inference services, and achieve private AI capabilities with data remaining within the internal network.