The next internet will not only connect people.
It will understand, assist, protect, and empower them.
Time of Idea To Code
*subject to conditions
runtime reliability
Our open, vendor-neutral compute platform that enables organizations to choose the right processor for the right workload. Rather than locking customers into a single hardware ecosystem, we support a broad range of GPU, CPU, and AI accelerator technologies, allowing you to optimize for performance, cost, energy efficiency, and reliability.
Our architecture intelligently aligns AI workloads, whether training, fine-tuning, inference, analytics, or HPC—with the most suitable compute resources. This workload-first approach ensures maximum performance while minimizing infrastructure costs and avoiding unnecessary overprovisioning. By embracing an open chip ecosystem, CloudworX gives enterprises the flexibility to leverage the latest innovations from multiple hardware vendors, future-proof their AI infrastructure, and maintain the freedom to scale without vendor lock-in.
we help connect the right chipset with an application for reliability, performance & cost.
Training large language models (LLMs), vision models, multimodal models, and domain-specific foundation models.
Supervised fine-tuning (SFT), reinforcement learning (RL), reinforcement learning from human feedback (RLHF), preference optimization, and continual model improvement.
High-performance model inference, Retrieval-Augmented Generation (RAG), AI agents, multi-agent systems, copilots, and autonomous workflows.
Computer vision, speech AI, recommendation engines, digital twins, scientific computing, simulations, financial modeling, life sciences, engineering.
AI is only as effective as the data that powers it. Before deploying models, agents, or GPU infrastructure, organizations need a trusted data foundation. CloudworX helps businesses prepare for AI by building secure, governed, and AI-ready data environments. Our approach starts with consolidating data, improving data quality, establishing governance, implementing secure storage, and enabling high-performance access across the enterprise. This ensures your data is accurate, protected, compliant, and ready for AI workloads.
From AI-native storage and distributed file systems to object storage and data governance, CloudworX provides the foundational infrastructure that transforms enterprise data into a strategic AI asset.
Provides scalable storage for AI models and unstructured data
Enables high-performance access for AI training and HPC workloads.
Stores embeddings for RAG, semantic search, and agent memory. Examples: Milvus, Qdrant.
Tracks data lineage, ownership, classification, and compliance. Essential for sovereign AI.
offering unified file and object storage with high throughput and low latency.
elastic, software-defined storage for AI applications and inference workloads.
distributed compute orchestration platform, enables workloads to run across clusters of machines, whether on cloud, on-premise, edge, or hybrid infrastructure.
Kubernetes enables distributed compute orchestration by managing workloads across multiple servers, clusters, edge locations, or cloud environments. It allows applications and AI workloads to run closer to where compute resources are available, making it a key foundation for cloud-native, edge, hybrid, and sovereign AI infrastructure.
Automatically runs containers across available compute nodes
Matches workloads with available CPU, GPU, memory, and storage
supports multi cloud infrastructure agnostic
Handles deployment, updates, rollback, and self-healing
Scales applications up or down based on demand
separates workloads using namespaces, policies, and access controls
CloudworX™
distributed compute orchestration platform, enables workloads to run across clusters of machines, whether on cloud, on-premise, edge, or hybrid infrastructure.
Kubernetes enables distributed compute orchestration by managing workloads across multiple servers, clusters, edge locations, or cloud environments. It allows applications and AI workloads to run closer to where compute resources are available, making it a key foundation for cloud-native, edge, hybrid, and sovereign AI infrastructure.
Training large models (LLMs) & heavy inference
Large-scale neural network training & deployment
Dedicated, ultra-fast specific workloads
On-device AI (smartphones, laptops, IoT)
CloudworX™
AI Sandbox provides a secure, controlled environment for testing AI models, agents, data pipelines, and enterprise workflows before production deployment. It enables organizations to validate performance, security, compliance, cost, and governance while protecting live systems and sensitive data.
AI workloads, runtime acceleration improves execution performance, and the AI Sandbox provides the safe environment to test, validate, and govern AI before production.
Test AI models without affecting live systems
Use masked, synthetic, or dummy data
Check accuracy, hallucination, bias, and reliability
Test AI agents before allowing them to act on real systems
Validate governance, audit trails, access control, and risk rules
Simulate real business processes before deployment
Train teams on AI tools without exposing production data
Test prompt injection, data leakage, API abuse, and model misuse
CloudworX™
The AI Command Center provides a unified dashboard to monitor, manage, secure, and govern AI workloads across distributed compute environments. It gives enterprises real-time visibility into GPU usage, model performance, agent activity, cost, compliance, security, and production readiness.
The cockpit for enterprise AI, giving organizations full visibility and control over models, agents, workloads, infrastructure, cost, risk, and governance.
Shows which AI models, agents, apps, and jobs are running
Tracks utilization, memory, temperature, latency, and availability
Shows token usage, GPU hours, inference cost, storage cost, and idle capacity
Tracks model accuracy, drift, hallucination risk, response quality, and latency
Shows what AI agents are doing, which tools/APIs they access, and their task status
Monitors access, data leakage risk, prompt injection, API abuse, and policy violations
Provides audit logs, approvals, reports, and governance evidence
Tracks which models/workflows are in testing, approved, or live
Sends alerts for failures, high cost, abnormal usage, security events, or SLA breach
Shows productivity gains, automation impact, user adoption, and ROI
From AI strategy to sovereign deployment , we help you adopt AI with confidence.