powerGraph -Unifying - Secure AI-accelerated data layer

[POWER - SCALE - COST]

    PowerGraph 

Unifying - Secure AI-accelerated DataLayer

AI-ready data solutions; Equitus.us, through its Knowledge Graph Neural Network (KGNN)  improves: regulated IBM Power 10/11 users, unregulated IBM Power 10/11, and x86 datacenters by providing a unifying, secure, and AI-accelerated data layer.

The core of Equitus's offering KGNN's  ability to automatically ingest, unify, and contextualize disparate data into an AI-ready knowledge graph, which is particularly optimized for the IBM Power architecture, specifically utilizing the Matrix Math Accelerator (MMA) on Power10/11 for high-performance, GPU-less AI at the edge or on-premises.

Equitus.us Value Proposition:  Shared and unique benefits across the three user groups:

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Overlapping Benefits Provided by Equitus.us

The main improvements that Equitus.us brings to all three groups (Regulated Power, Unregulated Power, and x86) center on data unification and AI readiness:

• Automated Data Unification (KGNN): Eliminates manual Extract, Transform, Load (ETL) pipelines by automatically ingesting, cleaning, and connecting structured and unstructured data from various sources into a single, cohesive knowledge graph.

• AI-Ready Intelligence: Transforms raw, fragmented data into vectorized, semantically indexed data, ready for advanced analytics, Machine Learning (ML), and Retrieval-Augmented Generation (RAG) pipelines without heavy pre-processing.

• Break Down Data Silos: Provides a single platform to connect legacy systems (often on Power) and modern systems (often on x86 or public cloud) to unlock hidden patterns and insights from the complete data ecosystem.

Shared Benefits for IBM Power 10/11 Users (Regulated and Unregulated)

• Power-Native Optimization: KGNN is built to run natively on Power10/11 servers, exploiting the architecture's features like the Matrix Math Accelerator (MMA) to deliver high-performance AI inference without needing expensive and scarce GPUs.

• Edge/On-Premise AI: Enables AI and deep learning to run efficiently at the edge or on-premise, leveraging the Power server's capabilities and keeping data local.

• Better Resource Utilization: Enhances the value proposition of the Power hardware investment by enabling next-generation AI workloads directly on the platform.

Shared Benefits for Unregulated IBM Power 10/11 and x86 Datacenters

• Simplified Hybrid/Multi-Cloud Strategy: Provides a consistent data-unification layer that can connect and contextualize data across the traditional Power environment and the more common x86/cloud environments.

• Faster Deployment of AI: Standardizes data preparation across mixed-vendor infrastructure, speeding up the development and deployment of AI/ML applications regardless of the underlying hardware.

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1. Data Sources (Top Row) | Box | Data Type | | :--- | :--- | | IBM Power 10/11 (Regulated & Unregulated) | High-Security Data | | x86 Datacenters (Modern & Legacy) | Disparate Data | | Edge / IoT (Data Streams) | Real-Time Streams |

2. Equitus KGNN Platform Core: Equitus Knowledge Graph Neural Network (KGNN) Specialization: POWER-NATIVE OPTIMIZATION (IBM Power MMA for GPU-less AI) Functions: Automated Ingestion, Semantic Unification, AI-Ready Vectorization

3. Outputs / Value Propositions | Box | Value Proposition | Driving Data | | :--- | :--- | :--- | | Next-Gen AI & LLMs (Enhanced RAG/ML) | Vectorized Intelligence | | Regulated Traceability (Data Provenance/Audit) | Semantic Context & Provenance | | Hybrid/X86 Analytics (Unified BI/Reporting) | Cross-Domain Data |

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