KGNN (kajun)
Knowledge Graph Neural Network
Knowledge Graph
Neural Network
Platform
Automated Data Structuring
for Enhanced AI
Automated Data Structuring
for Enhanced AI
KGNN, our Knowledge Graph Engine, automatically ingests, structures, and augments raw data.
It transforms your data into a semantically rich, machine-readable format optimized for AI processing and Retrieval-Augmented Generation (RAG) pipelines across diverse applications and microservices.
Key Features
๐น Automated ETL
๐น Autonomous Semantic Data Mapping
๐น Self-Generating Knowledge Graph Construction
๐น Instantly contextualize ingested data against a global knowledge base, providing immediate context and relevance.
Why KGNN?
๐น Break down Data Silos
๐น Easy Data Consolidation, Pre-Processing, and Enrichment On-Premise
๐น Boost and enhance your advanced applications with AI-ready, RAG-ready graph-contextualized data.
๐น Experience powerful querying and analytics.
Graph-Contextualized Data on the Fly
๐น Minimize manual data handling.
๐น Fuel your data science, analytics, and AI initiatives with comprehensive, relevant data that provides the whole picture.
๐น Reduce errors, improve accuracy, reduce bias, increase context, and enhance explainability for AI
AI-at-the-Edge Enabler
KGNN on IBM Power10 servers empowers organizations to create autonomous AI systems that operate at the edge, independently of external cloud services, and overcoming GPU resource limitations.
KGNN serves as a critical enabler in the edge AI landscape by ingesting,ย transforming, structuring, contextualized data directly at the edge.
The partnership with IBM amplifies this capability, offering a solution that is both cost-effective and energy-efficient without the need for GPUs.
Efficient Data Processing at the Edge
Facilitating AI-Ready Data
- Structured Output: Produces AI-ready, structured graph data that can be readily used by edge AI applications.
- Graph RAG Ready: Enhances retrieval-augmented generation systems by providing high-quality contextual data.
Optimized Performance without GPUs
- MMA Utilization: By leveraging IBM’s MMA, KGNN performs complex computations efficiently without relying on costly GPUs.
- Energy Efficiency: Reduced energy consumption makes it ideal for edge environments where resources are limited.
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Private and Off-Cloud Operations
- Data Sovereignty: Processing data locally ensures compliance with data privacy regulations and reduces security risks.
- Low Latency: Eliminates the need for constant cloud communication, resulting in faster data processing and real-time analytics.
