Whitepapers

Compass Use Case Guide | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

GenAI moves from interesting to essential the moment teams can point to specific, repeatable use cases that deliver business value. The challenge is rarely model capability; it is identifying the right starting points, mapping them to industry context, and running them in production securely and at scale. The Compass Use Case Guide is built to bridge that gap.

The guide opens with the six foundational GenAI patterns Compass is built to support: enterprise knowledge assistants, customer support and virtual agents, GenAI copilots for productivity, agentic AI workflows, AI content generation at scale, and retrieval-augmented generation applications. Each pattern represents a category of value, from grounding responses in trusted enterprise data to deploying autonomous agents that plan, reason, and execute multi-step tasks across systems.

From there, the guide maps GenAI into six industry verticals with concrete, deployable use cases. In telco and media, that includes real-time AI inference for customer interactions, speech analytics, churn prediction, billing optimization, and AI-driven traffic management. In healthcare, it covers medical image analysis, AI-powered diagnostics, clinical documentation, real-time patient monitoring, and triage assistants.

Public sector applications span predictive public safety, government contact centers, regulatory assistance, judicial case summarization, and traffic flow management. Banking and finance use cases include AI-powered fraud detection, virtual customer assistants, intelligent document processing, KYC/KYB onboarding, and credit risk insights, all areas where explainability and compliance matter as much as accuracy.

Manufacturing and energy round out the guide with use cases like energy consumption optimization, synthetic data generation, material science discovery, predictive maintenance, anomaly detection, real-time asset monitoring, and grid resiliency planning. Across every industry, the same Compass capabilities apply: access to leading models through one unified API, white-glove fine-tuning support, sovereign deployment with secure local integration, and a future-ready architecture that scales with evolving needs.

Core42 AI Cloud - Your gateway to the Future of AI | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

Core42 AI Cloud is a full-stack, AI-native cloud platform built for the full intelligence lifecycle, from training and fine-tuning to production-grade inference. The platform is anchored by two core services: GPU as a Service, providing direct access to a diverse range of accelerators via bare metal, Kubernetes, or Slurm orchestration, and Compass Inference as a Service, enabling teams to deploy and scale models with low latency, enterprise-grade performance, and built-in scalability.

The platform is engineered for accelerator choice without lock-in, supporting NVIDIA, AMD, Cerebras, Qualcomm, and Microsoft silicon so teams can match the right hardware to each workload. AI-optimized storage delivers fast, reliable access for AI and HPC data, designed for large-scale training and high-concurrency workloads without becoming a bottleneck and engineered with enterprise-grade resiliency for operational continuity.

Performance is validated by independent benchmarks. The Core42 Maximus-01 (AMD MI300X) system in the US ranks #20 worldwide on the Top500 HPC list and #3 worldwide on the IO500 storage benchmark. In the UAE, the Core42 NVIDIA DGX system ranks #37 globally (#1 in the UAE) and the Core42 AMD MI210 system ranks #38 globally (#2 in the UAE). These rankings reflect sustained system balance across compute, networking, and storage at production scale.

The brochure walks through the full architecture stack: GenAI services (agents, RAG, guardrails, fine-tuning, evaluation), model hosting and inference (model catalog, model-as-a-service), AI Ops (training, model customization, model governance), and infrastructure-as-a-service (compute, ultra-fast storage, high-speed networking, managed Kubernetes and Slurm, vector data management, access management, billing, and metering), all underpinned by a unified security and compliance layer.

The platform supports two primary consumption models. On-demand GPU instances give teams immediate access to diverse GPUs via the Core42 AI Cloud console with pay-as-you-go pricing and no long-term commitments, ideal for ML experimentation and inference. Large-scale GPU clusters provide reserved capacity for sustained training workloads, with managed Kubernetes and Slurm orchestration and high-speed networking over InfiniBand and Ethernet.

Built for global scale, Core42 AI Cloud operates 86K+ GPUs across sovereign data centers in the US (Buffalo, Minneapolis, Stockton, Sunnyvale, Dallas), UAE, Southern Europe, with Kenya, India, and SE Asia in active deployment. The brochure closes with the MBZUAI case study: how the Mohamed bin Zayed University of Artificial Intelligence trained frontier models including Jais, K2, and Jais Climate on a sovereign, heterogeneous compute environment combining NVIDIA DGX SuperPod with AMD MI210 GPUs hosted within the UAE, accelerating research timelines while keeping all nationally significant models under UAE jurisdiction.

Scaling Enterprise Intelligence with AI Cloud | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

Despite an estimated $30-40 billion in enterprise investment into generative AI, 95% of organizations have yet to see measurable return on their initiatives. The gap between AI ambition and AI ROI is not a model problem. It is an architecture problem. Pilots succeed in isolation, then stall when they meet the realities of enterprise data, governance, and integration. This roadmap is built for digital transformation leaders who need to close that gap.

The paper opens by examining why enterprise AI projects stall: a lack of unified strategy that creates fragmented stacks, data readiness gaps that undermine value and trust, compliance and sovereignty treated as afterthoughts, and a widening gap between AI talent and operating models. Each barrier is grounded in research from McKinsey, Gartner, Accenture, and others, with 65% of organizations yet to scale AI enterprise-wide and 77% of engineering leaders citing integration as a major challenge.

The central argument is a shift in mental model: AI must stop being a collection of disconnected projects and become the operating system of the business. That means AI embedded into workflows rather than sitting beside them, shared governed data rather than ad hoc extracts, a coherent full-stack platform rather than a toolbox, and reusable patterns rather than rebuilt-from-scratch use cases. Sovereignty and governance become foundational architecture, not procurement details.

From there, the paper provides a five-step blueprint for embedding AI into the business: define an AI strategy anchored in real workflows and KPIs, invest in AI-ready data and treat data as a product with documented lineage, plan AI with business, compliance, and risk teams in the room from day one, source a full-stack sovereign AI cloud platform that supports multi-accelerator workloads and unified orchestration, and integrate AI into systems of record like ERP, CRM, and HR so it can trigger and update real transactions.

The paper closes with how Core42 AI Cloud operationalizes this approach: a full-stack sovereign AI platform recognized by Top500 HPC and IO500, combining GPU-as-a-Service and inference-as-a-Service with unified orchestration, deep observability, and strict sovereignty controls. The result is a foundation for enterprises to move beyond experimentation and run AI as part of their production infrastructure, securely and at scale.

Learnings for Europe from the UAE on Scaling AI | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

Europe is at an AI crossroads. Despite ambitions for industrial renewal, productivity, and competitiveness, the continent's enterprises lag US peers in AI adoption, with 56% of 800 large European companies surveyed in 2025 yet to scale a major AI investment. Closing that gap could add nearly €200 billion to annual business revenues, according to Accenture. But the harder problem is structural: a looming infrastructure shortfall, energy grid constraints, and a compliance-led posture that slows execution. This EMIR Intelligence report, supported by Core42, examines what Europe can learn from a country that has solved many of these problems already, the United Arab Emirates.

The report opens by diagnosing Europe's AI deficit. The average European worker now produces only 76% of what a US counterpart produces per hour, and AI is the technology most likely to close that gap. Yet European firms invest less than US peers, contend with legacy data fragmentation, and operate under a comprehensive AI Act that imposes costs before tools reach production. Above all sits a problem of commitment, a failure to recognise the unique opportunity AI represents and to reshape economies around it.

It then turns to the looming infrastructure challenge. JP Morgan forecasts $5 trillion of investment in data centres over the next five years to meet global AI demand, with power generation as the binding constraint on where capacity gets built. Europe's ageing grids, high power prices, and renewable transition make the continent a difficult home for hyperscale buildout. In Ireland, data centres already account for more than a fifth of total power demand, and global tech firms are now under pressure to generate energy on-site rather than rely on the grid.

The UAE shows what is possible when a state mobilises behind AI. The country now ranks second globally for total AI compute capacity at 23.1 million H100 equivalents, behind only the US at 39.7 million, and sits in the top five of Stanford's Global AI Vibrancy index. A combination of empty desert, abundant low-cost energy, and early government commitment drew hyperscaler investment and enabled homegrown sovereign AI offerings, including Core42's AI Cloud, which combines access to NVIDIA, AMD, Cerebras, and Qualcomm accelerators with full data and workflow control. This has unlocked AI adoption in regulated sectors like energy and healthcare without requiring a comprehensive EU-style AI Act, because compliance is guaranteed by localisation.

The report closes with three recommendations for Europe: protect technological sovereignty by aligning EU governments, energy companies, and global tech firms to overcome grid and land use constraints; develop sovereign offerings, potentially through co-investments with experienced Gulf providers already expanding into Germany and France with next-generation data centres in Grenoble; and form intra-sector compacts where energy, health, and telecom leaders align on shared standards for sovereign AI ecosystems. The next two to three years will determine whether Europe builds the sovereign infrastructure to unlock an AI-enabled productivity surge, or drifts into dependency on external computing capacity and platforms.

Sovereign Public Cloud Brochure Download | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

Public sector and regulated industries are caught between two opposing forces. On one side, the pressure to modernize digital services, scale cloud adoption, and embed AI into decision-making is accelerating. On the other, governments and regulators demand stronger data sovereignty, tighter compliance, and clear control over sensitive information. Traditional cloud models force a compromise: public cloud delivers innovation but limits control, while private cloud delivers control but limits scale and agility.

Core42 Sovereign Public Cloud is built specifically to resolve that tension. It combines the full hyperscale capabilities of Microsoft Azure with UAE-centric sovereign and security controls, giving public sector and regulated organizations immediate access to the complete Azure product suite while maintaining the highest standards of in-country data sovereignty. Global innovation meets local expertise, without forcing a trade-off between the two.

At the heart of the platform is Core42 Insight, the application that makes sovereign compliance effortless to operate. Insight provides a comprehensive policy library mapped across UAE National Cloud Security Policy, Azure Confidential Compute, and national frameworks covering more than 200 technical controls. Real-time dashboards surface compliance posture, 30-day patterns, and non-compliant control flags so teams always know where they stand.

Beyond visibility, Insight actively accelerates resolution. AI-powered recommendations are context-aware and actionable, helping teams close misconfigurations and control gaps quickly. A visual resource landscape gives complete transparency into cloud assets, region distribution, and geographic compliance alignment, while proactive monitoring delivers on-demand compliance scans, configurable alerts, audit-ready reports, and structured exception management.

The brochure outlines how the platform applies across regulated use cases, from government entities managing confidential data within national borders, to financial institutions meeting data localization requirements, to healthcare providers protecting patient data through robust encryption and UAE-resident sovereignty. The outcome is consistent: organizations can innovate securely, govern confidently, comply continuously, and build trust at scale.

Balancing Innovation and Compliance in the AI Era | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

The UAE has set its sights on leading the next wave of digital transformation, with bold AI strategies, world-class data centres, and major investments in cutting-edge technology. Yet for highly regulated sectors, government, banking, healthcare, and oil and gas, that ambition collides with a difficult reality: data sovereignty, security, and compliance concerns have made public cloud adoption a careful balancing act between regulatory adherence and innovation.

This white paper, developed by Core42 and Microsoft with IDC research, examines how sovereign enabled public cloud is resolving that tension. Traditional sovereign cloud has meant private, often air-gapped environments that deliver control but limit scale and innovation. Sovereign enabled public cloud takes a different approach: combining hyperscale public cloud infrastructure with local sovereign and security controls, giving regulated organizations both regulatory assurance and access to the full pace of cloud and AI innovation.

The paper grounds the discussion in IDC market data, including UAE public cloud spend on track to reach $6.47 billion by 2028 at a 21.7% CAGR, the top three factors organizations consider when migrating (innovation, cost, compliance), and global sovereign cloud spending forecast to nearly double from $133 billion in 2024 to $259 billion by 2027. It maps the two sovereign cloud models, sovereign-by-design private cloud and sovereign enabled public cloud, across infrastructure, sovereignty level, innovation potential, compliance, and value for money.

It then walks through how sovereign enabled public cloud applies across four regulated industries. Banking and finance teams operating under CBUAE, ADGM, and DIFC mandates can run core systems and AI-driven services compliantly. Government entities pursuing initiatives like Abu Dhabi's AI-native government strategy can modernize without compromising data sovereignty. Healthcare providers can meet UAE Healthcare Data Law requirements alongside HIPAA and GDPR while enabling AI-driven diagnostics. Oil and gas organizations can run real-time analytics and predictive maintenance across geographically dispersed operations.

The middle of the paper is a practical migration playbook: classifying data using the UAE Smart Data Framework's four levels (open, confidential, secret, top secret) to choose the right cloud model, selecting workloads for phased migration, defining KPIs across innovation and compliance, and evaluating providers on the synergy between global hyperscaler capability and local sovereign expertise.

The paper closes with the Core42 Sovereign Public Cloud solution itself, the partnership with Microsoft Azure, the role of the Insight application in automating sovereign controls and compliance reporting, and an independent 2024 study of 1,954 EMEA organizations that ranked Microsoft Azure best-in-class for sovereignty alongside seven other attributes. The closing section gives tech buyers five strategic actions for leading in a sovereignty-first digital economy.

Compass Brochure Download | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

Most enterprises hit the same wall when moving GenAI from pilot to production: the trade-off between speed and control. Open access to frontier models comes at the cost of sovereignty, governance, and predictable cost. Building those controls in-house slows everything down. Core42 Compass is built to remove that trade-off.

Compass is a full-stack GenAI platform that gives developers a single API to access 50+ leading open and closed-source models, including offerings from OpenAI, Anthropic, Meta, Mistral, DeepSeek, xAI, and JAIS. Teams can switch models without rewriting their stack, compare performance side-by-side in the Compass Playground, and scale from prototype to high-throughput inference processing hundreds of millions of tokens in minutes.

Sovereignty is built into the architecture, not bolted on. Compass runs in Azure UAE with in-country data residency, encryption at rest and in flight, SOC2 Type II compliance across 170 policies, and zero customer data logging. That means regulated and high-trust environments can run frontier GenAI without compromising on data protection or compliance posture.

Beyond access, Compass includes the operational layer enterprises actually need to run GenAI responsibly: resource and usage management across teams, built-in metering and billing visibility, secure private endpoints, access controls, and guardrails. Combined with 24/7 support and 99.5% uptime, teams get startup speed with enterprise-grade control.

Production workloads are supported through batch processing APIs for high-volume inference, fine-tuning services to customize models to specific domains, and agentic frameworks for building autonomous multi-step workflows. The brochure walks through six common build patterns, from enterprise knowledge assistants and RAG applications to GenAI copilots and agentic AI workflows.

How Core42 AI Cloud Industrializes Intelligence at Frontier Scale | Core42 Whitepapers

Core42 — Tue, 28 Apr 2026 17:31:00 GMT

AI is entering an industrial phase. Models are no longer experimental artifacts; they are foundational assets powering public services, economic productivity, and national competitiveness. Yet the prevailing assumption that frontier-scale AI can be achieved by aggregating GPUs has proven incomplete. Performance, cost efficiency, and time to value are constrained far more by how intelligence is generated, transported, governed, and consumed than by raw compute availability alone.

This paper opens by diagnosing why hardware-centric approaches fail at scale. In GPU-centric cloud models, compute, networking, storage, and orchestration are optimized independently, often across different stacks. At frontier scale that fragmentation compounds into underutilized accelerators, slower convergence, costly recovery delays, and operational overhead that grows faster than model complexity. The result: infrastructure friction, not model innovation, becomes the limiting factor.

The paper then introduces a different paradigm: intelligence as a distributed system, not a local asset. Heavy compute workloads (training, large-scale fine-tuning, foundation model optimization) can be centralized where economics are optimal, while inference and consumption happen wherever required. This is the foundation of the G42 Intelligence Grid, with Core42 AI Cloud as its intelligence production layer.

From there, the paper details the architectural foundations of Core42 AI Cloud: heterogeneous bare-metal compute supporting NVIDIA, AMD, Cerebras, and Qualcomm silicon; high-speed InfiniBand and Ethernet/RoCE networking engineered for AI communication patterns; a unified AI-optimized storage architecture spanning staging, scratch, and backup tiers; and orchestration that unifies bare-metal GPU provisioning with managed Kubernetes and Slurm under a single operational fabric.

Performance is grounded in independent benchmarks. The Core42 Maximus-01 system ranks #20 worldwide in the Top500 HPC list at 114.50 PFlop/s Rmax across 976,896 cores, with the NVIDIA DGX system at #37 globally and the AMD MI210 system at #38. In the IO500 storage benchmark, Core42-powered systems rank #3 globally. These results demonstrate sustained system balance, the condition for both velocity and economic viability at scale.

The paper closes by showing how Compass, the intelligence consumption layer, completes the lifecycle: production-grade inference, batch processing APIs, agentic frameworks, and fine-tuning services translate trained intelligence into developer-ready capabilities. Together, Core42 AI Cloud and Compass close the loop from training to impact. Real-world deployments at institutions like Mohamed bin Zayed University of Artificial Intelligence demonstrate the platform operating intelligence as an industrial capability, not an experiment.