Pain Points & Solutions
Core pain points in research work
Starting from data silos, the statistical barrier, and multicenter collaboration, we build an end-to-end research-data chain.
Serious data silos
Scale systems, HIS, and imaging devices each stand alone; integrating data requires extensive manual import and cleaning
A unified research data platform
Multi-source data is aggregated and mapped automatically, so researchers can focus on analysis rather than data preparation
A high bar for statistical analysis
Clinicians excel at clinical work but not at statistics, and must rely on professional statisticians
AI-assisted analysis
Describe analysis needs in natural language; the system generates statistical plans and visual results automatically
Difficult multicenter collaboration
Cross-institution studies lack a unified platform for data transfer, access control, and progress sync
A multi-tenant collaboration system
A unified platform supports cross-institution work, with fine-grained data permissions and real-time progress transparency
Core Capabilities
Core capabilities
Research data management as the foundation, connecting analysis tools and collaboration platforms to raise research efficiency.
Data management
Unified collection and governance of heterogeneous, multi-source data
- Scale data
- Physiological signals
- Imaging data
- Omics data
Statistical analysis
Visual analysis tools that lower the statistical barrier
- Descriptive statistics
- Difference analysis
- Regression models
- Machine learning
AI-assisted research
Support for literature review, protocol design, and manuscript writing
- Literature search
- Protocol generation
- Statistical analysis
- Writing support
Multicenter collaboration
A cross-institution collaborative research platform
- Project management
- Data sharing
- Access control
- Progress tracking
Technical Highlights
Technical highlights
AI-assisted research and multi-source data fusion, accelerating scientific output.
AI research assistant
LLM-driven research support for literature reviews, protocol design, statistical-analysis code generation, and writing assistance
Multi-source data fusion
Unified management and cross-analysis of multimodal data including scales, physiology, imaging, and omics
Compliance and privacy
Data de-identification, federated learning, and differential privacy support multicenter research while protecting participant privacy