Equitus.us addresses the challenge of fragmented enterprise data. [PowerGraph] on IBM enable Data Unification by connecting AI agents by leveraging its Knowledge Graph Neural Network (KGNN) platform, which is natively optimized for IBM Power 10/11 servers.
[PowerGraph] Understands and integrates multiple machine learning pipeline in a normalization data fabric, allow for the visualization of ai agents into NLP messages to: Perceive, Process and Act
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The [PowerGraph] fundamental solution lies in using the capabilities of KGNN on IBM Power 10/11 systems to perform context engineering at scale:
1. Automated Contextualization via KGNN (PowerGraph)
Equitus's KGNN platform is designed to directly solve the problem of fractured data by automating the process of building a semantic layer—a crucial step for giving AI accurate context.
Automated Data Unification: KGNN automatically ingests, cleans, and unifies both structured and unstructured data from disparate systems (databases, documents, logs, etc.) into a schema-less knowledge graph. This eliminates the manual, fragile ETL pipelines and custom ontology building typically required.
Semantic Layer Creation: It extracts entities, relationships, and context from the raw data, transforming disconnected fragments into an interconnected web of knowledge. This preserves the business context, allowing the AI to understand how one piece of information relates to the rest of the organization's data.
AI-Ready Data Generation: The platform generates vectorized, semantically indexed data (graph embeddings). This structured, contextual data is then directly usable by Large Language Models (LLMs) and AI agents, specifically powering Retrieval-Augmented Generation (RAG) pipelines for grounded, accurate, and explainable AI responses.
2. Optimization on IBM Power 10/11
The partnership between Equitus and IBM is critical, as it provides the high-performance, secure, and flexible infrastructure needed for large-scale context engineering.
Native Optimization for AI Inference: KGNN is built to run natively on IBM Power 10 and Power 11 servers, leveraging their on-chip Matrix Math Accelerator (MMA) technology. This provides high-performance AI inference (running the models) without the need for expensive, dedicated GPUs, significantly reducing cost and power consumption.
Edge-Readiness and Data Sovereignty: The ability to run KGNN efficiently on Power servers, especially the smaller Power S1012/S1122 models, makes it edge-ready. This allows organizations to process and contextualize data where it is generated, reducing latency, ensuring data sovereignty, and meeting security and regulatory requirements by keeping sensitive data on-premises.
Consolidation and Efficiency: By using Power 10/11's architecture, Equitus provides a unified platform for both data storage/processing and AI model inferencing, simplifying the infrastructure stack and maintaining high availability (e.g., up to 99.9999% uptime with Power11 features).
3. KGNN Software Architecture for IBM Power 10/11
Equitus KGNN is built as an AI-Ready Data Unification Platform with a focus on enterprise-grade performance, security, and traceability on the IBM Power platform.
Power-Native Optimization: KGNN is specifically built to run natively on IBM Power10/11 hardware, directly leveraging the on-chip Matrix Math Accelerators (MMA). This allows it to perform complex neural network and graph computations at high speed without relying on costly, power-intensive GPUs.
Automated Data Structuring: It employs Automated ETL (Extract, Transform, Load) and Autonomous Semantic Data Mapping to ingest raw data (structured, unstructured, logs, PDFs) and automatically identify, clean, and connect entities and relationships. This eliminates the massive pain point of manual data preparation.
Schema-less Knowledge Graph: The platform builds a self-generating knowledge graph that does not require a fixed, predefined schema. This makes it highly flexible and scalable, capable of integrating new, evolving datasets without requiring constant rework and refactoring.
AI-Ready Output: The graph data is automatically transformed into a semantically rich, machine-readable format, outputting vectorized graph data ready for use in advanced AI applications, including Retrieval-Augmented Generation (RAG) pipelines for Large Language Models (LLMs). This ensures the AI is grounded in the organization's trusted, contextualized data.
Microservices/Open Architecture: The platform is comprised of a family of Kubernetes-based interconnected micro-services (often deployed with Red Hat OpenShift), allowing for rapid deployment, flexible scaling, and integration with existing enterprise tools and applications.
In summary, Equitus.us solves the enterprise context problem by deploying its PowerGraph (KGNN)—a graph database and AI platform—on IBM Power 10/11, turning the monumental task of connecting everything into an automated, high-performance, and secure operation that delivers AI-ready, semantically rich data. This fundamentally anchors enterprise AI in relevant and meaningful context, moving it beyond "guessing in the dark" to accurate understanding.
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