Data silos, uncontrolled quality, lack of governance — these systemic obstacles are silently evaporating massive AI investments.
Data is still fragmented, but AI needs a global view. Traditional platforms only "store" but don't "connect" — without breaking data silos, AI implementation always falls short.
You feed AI your data, but it speaks someone else's language. Private domain data isn't understood, hallucinations become the norm — no matter how powerful the model, it can't save an AI that "doesn't understand you."
Using marathon shoes to play basketball. When traditional architecture meets AI vector retrieval, high concurrency crashes and storage cost explosions follow — without architecture change, performance bottlenecks remain.
More and more data accumulates, but less and less is usable. A data platform without governance is a swamp — AI sinks deeper, models drink dirty water, output is naturally waste.
AI is manipulating your most core data, but you can't see what it's doing. No audit, no traceability, no access control — this isn't AI risk, it's out of control.
To use AI, first spend double the money on supplementing data. Dual storage, dual synchronization, dual maintenance — for every AI scenario added, another set of invisible bills. Money spent, value discounted.
From data ingestion to intelligent applications, creating AI-native data infrastructure across the full chain
Unified data hub, dynamic integration of heterogeneous sources, breaking through data barriers. Solves core problems in multi-source data ingestion, unified metadata management, cross-source query collaboration, and full-chain tracking governance.
Ultimate performance engine, billion-level data queries in seconds, storage-compute separation. Through storage tiering, format optimization, and compute acceleration, solves slow queries and low analysis efficiency with large data volumes.
Built on private domain data, more accurate and faster output, near-zero hallucinations. Full-chain AI support from feature engineering, model training/fine-tuning to underlying toolkits.
Autonomous planning and execution, no-code data engineering. Understands, plans, executes and optimizes complex data-related tasks, forming a complete data workflow closed loop.
Providing customized solutions for different industry data characteristics and business requirements
Multi-campus HIS/EMR/LIS data silos are obvious, unstructured medical records are difficult for models to understand. Fabric uniformly connects heterogeneous medical data sources, semantic layer standardizes ICD/SNOMED codes, vector retrieval supports hospital operations management and research.
Real-time risk control and offline reporting run in parallel, data consistency and regulatory audit chains face challenges. HTAP storage handles both transactions and analysis, full-chain Tracing meets audit requirements, Feature Store unifies risk features and maintains online/offline consistency.
Massive IoT sensor data real-time ingestion pressure is high, equipment predictive maintenance model training cycles are long. Connectors support real-time streaming ingestion, tiered storage reduces cold data costs by 30-50%, Fine-tuning quickly builds equipment fault prediction models.
Rail, road, aviation and other multi-modal travel data is fragmented, cross-source joint analysis is difficult. Unified query engine supports cross-source analysis, Data Catalog inventories data assets, Data Agentic automatically generates capacity scheduling insight reports.
Cross-departmental data sharing compliance barriers are high, data openness and privacy protection are difficult to balance. Fine-grained permission validation, Tracing audit and MCP Tools low-code development capabilities balance security compliance and application implementation.
Process parameters, quality inspection and supply chain data are three types of heterogeneous data that are difficult to collaboratively model. Fabric connects structured and unstructured data, vector storage supports semantic retrieval, Data Agentic covers the full chain from collection to model deployment.
Delivering verifiable excellence through industry-leading technical indicators and engineering practices
Stay informed about Nexus Evo's latest progress and industry trends
New version enhances multi-source data ingestion capabilities, supports seamless connections to over 200 data sources, further improving metadata management efficiency.
Read MoreGathered 100+ industry partners to explore new paths for data intelligence transformation, launching multiple joint solutions on site.
Read MoreWith innovative product architecture and excellent performance, won the annual best AI data infrastructure award from industry authority.
Read MoreWorking with partners to advance data intelligence transformation and create value together
Establishing deep technical cooperation with major cloud vendors, AI platforms, and database manufacturers to create optimal solutions together.
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Establishing strategic partnerships with industry-leading enterprises to jointly promote industry standardization and large-scale applications.
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