Our system architecture is built on a robust six-layer design that ensures high performance, seamless integration, and resilient scalability across complex industrial and laboratory environments.

This architecture integrates domain-specific microservices under a unified, AI-empowered digital framework, enabling agile battery R&D, high-throughput testing, and data-driven decision-making. From laboratory information management to optimization, machine learning, and real-time analytics, every module is designed for scalability, modularity, and continuous intelligence—supporting next-generation energy technologies with unmatched digital capability.

By integrating project planning, DOE, and battery designer platforms, this dimension extends the LIMS core to cover structured task generation and parametric mapping. It establishes a digital twin of experiment intent before execution, ensuring high-efficiency orchestration and robust traceability across all upstream processes.

QuaNode is a powerful edge computing unit designed for battery testing labs and production environments. It connects seamlessly with diverse testing devices via common IoT protocols, translating vendor-specific commands into unified workflows. Equipped with local data processing, it performs feature extraction, anomaly monitoring, and diagnostics directly on the edge, ensuring low-latency insights and high resilience. Models trained from full-scale cloud data can be deployed back to QuaNode, enabling real-time inference and continual improvement at the edge—thus forming a closed-loop intelligent quality system from data acquisition to autonomous decision-making.

Leveraging LIMS-collected data, this axis expands the post-experiment value chain through OLAP, statistical analytics, and machine learning. It drives value realization from raw data to decision-grade insights, enabling anomaly detection, causal analysis, and predictive modeling in a closed feedback loop.

The AI Agent is not just a general-purpose LLM assistant, but an orchestrator that embeds domain-specific logic and custom data processing pipelines. It enables intelligent assistance across the entire workflow—from project planning to test result analysis—by integrating natural language understanding with executable logic blocks. It can parse technical documents, generate structured experiment plans, trigger complex multi-step analyses, and summarize insights.

Critically, the agent supports calling custom scripts and workflows for processing business data and complex multiscale electrochemical testing datasets. These include physical model-based feature extractions, cross-resolution data alignment, and anomaly interpretation in highly non-linear systems—bridging the gap between domain expertise and AI-driven automation.