strategic integration between e& (formerly Etisalat Group), Equitus.ai, and IBM Power 10/11

Proposal;  AIMLUX.ai offers to develop consulting solutions. Exploring - Strategic integration between e& (formerly Etisalat Group), Equitus.ai, and IBM Power 10/11 hardware creates a high-performance ecosystem for "Agentic AI" and secure data processing.  

By leveraging Equitus’s Knowledge Graph Neural Network (KGNN) on IBM’s latest silicon, e& can ingest vast amounts of telecommunications and enterprise data without the latency and cost of cloud-based GPU clusters.

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How e& Integrates Equitus.ai on IBM Power

1. Automated Data Ingestion (KGNN)

Equitus.ai tools provide a "zero-ETL" (Extract, Transform, Load) environment.¹ For a massive data holder like e&, this means:

• Schema-less Unification: Ingesting structured (databases) and unstructured (PDFs, logs, call records) data into a single semantic knowledge graph without manual pipeline building.²

• Native Power Execution: Unlike most AI tools that require emulation or heavy containerization layers, KGNN runs natively on AIX and Linux on Power, allowing e& to utilize the hardware's full bandwidth.³

2. Leveraging the Matrix Math Accelerator (MMA)⁴

The core of this synergy is the Matrix Math Accelerator (MMA) found in IBM Power 10 and Power 11 processors.⁵

• No GPUs Needed: Equitus tools are optimized to use MMA for AI inferencing directly on the CPU.⁶ This allows e& to run deep learning and vectorization at the "edge" or in local data centers without the expense of NVIDIA GPUs.⁷

• Increased Throughput: Power 10/11 provides significantly higher performance-per-core for the matrix-heavy math required by Equitus's neural networks, essential for e&'s real-time risk and compliance monitoring.⁸

3. Agentic AI & Watsonx Integration

As of January 2026, e& and IBM have partnered to build Agentic AI solutions.⁹ Equitus.ai acts as the "intelligence layer" within this framework:

• Semantic Layer for Watsonx: Equitus provides the semantically rich, machine-readable data that feeds into IBM watsonx.governance and watsonx Orchestrate.

• Actionable Insights: This allows e&'s AI agents to not just answer questions, but to reason across disparate data silos (like legal, regulatory, and technical logs) to perform tasks autonomously.

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Technical Architecture Overview

Feature	Implementation for e&
Hardware	IBM Power S1022 or Power11 entry servers.
Software Stack	Equitus KGNN + Red Hat OpenShift on Power.
Ingestion Method	Semantic extraction of entities and relationships directly from e& data buckets.
Security	Transparent Memory Encryption (Power 10/11 feature) ensures data is secure even while being processed by the AI.
Primary Use Case	Policy, Risk, and Compliance (GRC) automation.

Key Benefits for e&

• Data Sovereignty: Since Equitus on Power doesn't require cloud dependencies, e& can keep sensitive regional telecommunications data strictly on-premises in the UAE and other markets.¹⁰

• Energy Efficiency: IBM Power 11 offers improved performance-per-watt, reducing the carbon footprint of e&’s large-scale AI operations.¹¹

• Rapid Deployment: Using preconfigured "KGNN appliances" on Power hardware allows e& to move from a Proof of Concept (PoC) to full production in weeks rather than months.¹²

Would you like me to look into the specific performance benchmarks of Equitus KGNN on Power 11 compared to traditional x86 setups for telco workloads?