Key Confirmations and Context
Core Technology: Equitus uses its Knowledge Graph Neural Network (KGNN®) platform to ingest and transform data. KGNN is a self-generating, schema-less knowledge graph engine that automatically unifies, structures, and augments raw, fragmented data into an AI-ready, semantically rich format by extracting entities, relationships, and context.
Platform Optimization: The KGNN solution is confirmed to be Power-native, running directly on IBM Power10 (and being adapted for Power11) servers. A key optimization is the use of the IBM Power10's Matrix Math Accelerator (MMA), which enables high-performance AI inferencing and deep learning without the need for external GPUs or cloud dependencies.
Primary Benefits and Use Cases:
Data Unification and Contextualization: It breaks down data silos and transforms disparate enterprise data into a single, comprehensive, and contextualized knowledge graph.
On-Premise Security: The ability to run natively on Power systems without the cloud allows enterprises in regulated industries (like defense, finance, and intelligence, where Equitus has a strong background) to maintain data sovereignty and on-premise security for their sensitive data.
AI/Agentic Functions: The output, being vectorized and semantically indexed, is explicitly optimized for Retrieval-Augmented Generation (RAG) pipelines and AI agents/LLMs (empowering agentic functions), enabling NLP and NLQ (natural language querying) on highly secure, on-premise infrastructure.
Industrial Applications: The solution is aimed at enterprise applications across sectors like defense, intelligence, financial services, healthcare, and critical infrastructure, helping to improve operational effectiveness through real-time intelligence and decision-making.
The video below provides an introduction to the Equitus KGNN platform.
Introducing the World's First Knowledge Graph Neural Network Platform
For A pilot discussion - Contact David Zlotolow - ZlotolowD@equitus.us
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