[POWERGRAPH] provides clear and useful way to support a wide range of customers needs with Equitus.us,

[POWERGRAPH] provides clear and useful way to support a wide range of customers needs with Equitus.us, which provides AI-ready data and Knowledge Graph Neural Network (KGNN) platforms, often leveraging IBM Power systems.

PowerGraph - three categories, focusing on the key client characteristics and technology use cases:

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1. IBM Regulated (Finance, Healthcare, Gov/Def)

This category represents customers in highly sensitive sectors with strict compliance, security, and data governance requirements.

• Client Characteristics:

  • Focus: Security, data sovereignty, regulatory compliance (e.g., GDPR, HIPAA, FedRAMP, Basel III).

  • Need: Traceability, explainability, and immutability of data and AI models. Equitus.us's focus on Traceability & Explainability and solutions like Equitus Video Sentinel (EVS) for defense are highly relevant here.

  • Infrastructure Preference: On-prem or highly secure hybrid cloud due to sensitive data, often running mission-critical workloads on-premises.

• Technology & Use Case:

  • IBM Systems: Primarily Power10/11 systems, leveraging their high performance, resiliency, and integrated security features like Power Cyber Vault for enhanced data protection and fraud detection, risk management, or intelligence analysis.

  • Equitus Value: The KGNN platform provides a unified, semantic knowledge graph that helps with compliance audits, connecting disparate legacy and modern systems, and real-time security and anomaly detection, which is crucial in these sectors. The native optimization for IBM Power10/11 delivers high-performance deep learning without GPU dependency on the edge, enabling lower costs and full data control for regulated workloads.

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2. IBM Non-Regulated (Retail, Logistics, Travel)

This group consists of enterprises focused on efficiency, customer experience, and operational transformation, with less strict external regulatory oversight than the first category.

• Client Characteristics:

  • Focus: Operational efficiency, supply chain optimization, predictive analytics, customer-facing AI (e.g., personalization, demand forecasting).

  • Need: Scalability, faster time-to-insight, and competitive advantage through AI.

  • Infrastructure Preference: Flexible Hybrid Cloud model to scale AI and analytics deployments quickly.

• Technology & Use Case:

  • IBM Systems: Power10/11 systems for core enterprise resource planning (ERP) or large database applications, often integrated with IBM Cloud or Red Hat OpenShift for containerized AI services.

  • Equitus Value: The automated data structuring and Knowledge Graph generation significantly speed up data prep and the deployment of AI models for use cases like inventory management, route optimization, and customer segmentation. Their platform eliminates data bottlenecks and helps unify complex supply chain and retail data.

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3. X86 DataCenters/Cloud (GPU, CPU, and ARM - Dell/HPE and Databricks/Snowflake)

Standard cloud and commodity hyperscalers- hardware infrastructure, often leveraging specialized hardware for AI/ML workloads.

• Client Characteristics:

  • Focus: Cloud-native architecture, utilizing a multi-cloud or hybrid cloud approach, and seeking maximum price-performance for generalized and specialized compute.

  • Need: Access to vast pools of computational resources (especially for AI training), ease of deployment across popular cloud environments (e.g., AWS, Azure, Google Cloud).

  • Infrastructure Preference: Commodity x86 servers (Dell/HPE) for general compute and the rapidly growing segment of ARM-based CPUs (e.g., AWS Graviton, NVIDIA Grace) for power efficiency, alongside dedicated GPUs for acceleration.

• Technology & Use Case:

  • X86/ARM/GPU: Use of mainstream platforms and cloud data services like Databricks or Snowflake for large-scale data processing and AI/ML model training/serving. x86 remains dominant for general data center CPUs, but ARM-based CPUs are rapidly gaining share, especially in hyperscale clouds and as host chips for NVIDIA GPUs.

  • Equitus Value: While Equitus.us has strong IBM Power-Native software solutions, their KGNN platform is also available Hybrid, or Cloud, which is critical for supporting these customers. The value proposition here shifts slightly to leveraging the Equitus platform to unify and contextualize data regardless of the underlying cloud vendor or processor architecture, providing a single, trustworthy data layer for federated queries and analytics across their fragmented data landscape.