As a core financial institution rooted in an international financial hub and serving both urban and rural areas, the Bank has long been committed to promoting inclusive finance. However, its intelligent customer service system—operating for nearly a decade—had become a significant “technical debt,” constraining service efficiency and system stability due to its outdated architecture, maintenance challenges, and insufficient disaster recovery capabilities.
To achieve a breakthrough in customer experience (CX), the Bank partnered with Cloopen Cloud to launch a critical “Dual-Active Architecture Transformation and Full Lifecycle Replacement” project.
The project’s primary goal was to replace the legacy system without any business interruption, and to build a next-generation AI service platform—transforming from “available” to “highly available” and from “passive maintenance” to “self-evolving.”
The system relied on regular expression matching, lacking natural language understanding capabilities and support for contextual, multi-turn conversations—resulting in a limited customer experience.
Weak vendor support led to heavy dependence on manual updates for the knowledge base, resulting in low operational efficiency.
The absence of a dual-active architecture meant poor disaster recovery. Any system failure could lead to service interruptions.
The project required a full replacement of a decade-old, closed, and heterogeneous system—while ensuring zero disruption to tens of thousands of daily customer interactions.
Faced with a closed technology stack and incomplete documentation from the previous vendor, Cloopen Cloud quickly dissected the system logic and established a sustainable and controllable maintenance framework.
By running both the old and new systems in parallel, the team achieved a zero-downtime switch, effectively avoiding common pitfalls such as data inconsistencies and service disruptions.
Extensive business knowledge, unique Q&A data, and operational logic accumulated in the legacy system were thoroughly analyzed and migrated losslessly, ensuring the new system was ready for “plug-and-play” use.
Through multi-center deployment, real-time data synchronization, and rapid failover mechanisms, the project established a robust foundation for long-term system high availability.
Next-Generation Semantic Understanding:
Transitioned from rule-based matching to deep learning-based semantic recognition, fully supporting contextual, multi-turn conversations for a natural, human-like interaction experience.
Localized Adaptation:
Enhanced compatibility with dialects and non-standard financial terminology to accurately interpret region-specific inquiries and increase user affinity.
End-to-End Efficiency:
Enabled automated management of FAQs—from creation and training to deployment—boosting operational efficiency by over 50%.
Continuous Learning Optimization:
Through automatic question generalization and synonym management, the platform continuously retrains and optimizes models, improving recognition accuracy over time.
Intelligent Labeling and Model Refinement:
Automatically clusters unrecognized queries and applies intelligent tagging to quickly close the optimization loop.
Multi-Dimensional Data Insights:
Provides real-time metrics on customer traffic, trending issues, and chatbot satisfaction, empowering data-driven business decisions.