KGNN - Knowledge Graph Neural Network Discussions

Equitus KGNN Automated Data Unification & Semantic AI Platform Equitus KGNN (Knowledge Graph Neural Network) is a next-generation AI-ready data platform that unifies, structures, and contextualizes data—automatically. Built to overcome the limitations of legacy ETL, siloed systems, and batch processes, KGNN turns raw, unstructured, and fragmented enterprise data into enriched, AI-ready knowledge. We built KGNN to automatically transform the overwhelming volume of disparate enterprise data—far beyond what humans can process manually. Our AI-driven platform handles it in real time, enabling automated ingestion and semantic structuring at scale. What KGNN Delivers: Automated Data Ingestion – No traditional ETL needed. Semantic Auto-Mapping – Self-constructing RDF knowledge graph. Unstructured Data Transformation – Turn PDFs, emails, logs, and text into structured, vectorized knowledge. Federated Bi-Directional Integration – Seamlessly sync with legacy and modern systems. Core Capabilities: Break down silos and unify data across legacy + modern systems. Add semantic structure and context for GenAI, BI, LLMs, and more. Enable real-time AI-ready data across your enterprise. Ensure trust, precision, explainability, and privacy in AI systems. Optimize cost and performance with deployment on IBM Power10 and other edge-ready infrastructures. Why It Matters: - Replace expensive, fragile custom connectors. - Contextualize data instantly for LLMs and analytics. - Eliminate delays from batch data handling. - Run on-prem, at the edge, or disconnected environments with full security and governance. KGNN brings context to your data and clarity to your decisions, on a platform built to scale, adapt, and secure your entire data lifecycle. Minimum Specifications IBM Power10/11 40 Cores 512GB RAM 4TB SSD (usable) RedHat OpenShift 4.18 X86/GPU 24 Cores 256GB RAM Nvidia GPU w/ 24GB+ 4TB SSD (usable) RedHat OpenShift 4.18 Equitus KGNN is optimized for both high-performance enterprise hardware and energy-efficient edge deployment. When users leave KGNN - Knowledge Graph Neural Network reviews, G2 also collects common questions about the day-to-day use of KGNN - Knowledge Graph Neural Network. These questions are then answered by our community of 850k professionals. Submit your question below and join in on the G2 Discussion.