HCLTech’s AI Factory, powered by NVIDIA and Microsoft, is a full‑stack, enterprise‑grade AI infrastructure and operational framework designed to help organizations build, scale, and operate AI at production level. The offering brings together accelerated computing, advanced networking, AI/LLM tooling, MLOps/LLMOps, and turnkey use‑case accelerators, integrating seamlessly with cloud, hybrid, on‑prem, and edge environments.

HCLTech’s AI Factory is a comprehensive, production‑ready AI ecosystem that combines NVIDIA’s and Microsoft full‑stack accelerated computing with HCLTech’s proprietary AI engineering, operations, data platform, and Physical AI innovations—enabling enterprises to build, scale, and operationalize AI across cloud, edge, and hybrid environments with speed, safety, and repeatability.

Core Architecture for AI Factory / AI driven Cognitive Infrastructure Includes:

Accelerated Computing - NVIDIA GPUs, GPU drivers, GPU Operator
AI Infra Platform - Azure Local, High‑Performance Storage, Azure networking, Azure security Services
NVIDIA AI Enterprise (NVAIE)- NeMo (Retriever, Customizer, Curator, Guardrails), NIM (Inference microservices), Base Command Manager, Run.ai integration for GPU orchestration
Physical AI - Omniverse, Isaac, GR00T, Cosmos, Metropolis
AI Data Platform - VectorDB (cuVS), Azure Databricks, Microsoft Fabric, Azure Data Lake
Ops Stack - LLM Ops, ML Ops, Data Ops, Infra Ops, FinOps
Observability: Dynatrace, Datadog, Azure AI
Security/Governance with Purview + Responsible AI
HCLTech IP Accelerators - IntelliOps, ePACE, eAssist, IDP, SuperBot, CodeProbe

This architecture provides a repeatable, reference‑driven blueprint for deploying enterprise AI across industries (Manufacturing, BFSI, Telecom, Healthcare).

What the HCLTech–NVIDIA AI Factory Enables

A. Scalable AI Infrastructure

AI Factories built using NVIDIA & Microsoft Azure validated designs
Full-stack integration of hardware + networking + software + deployment patterns
Multiple consumption models: on‑prem, hybrid, multi‑cloud, edge

B. Production‑Grade AI / LLM

LLM training & deployment via NeMo + HCLTech AI Engineering+ Azure AI Services
Multimodal pipelines, PDF blueprinting, Microsoft + NVIDIA synergy (e.g., GH Copilot + Azure AI services)]
Data lifecycle management (AI Foundry + data platform)

C. Physical AI (Unique Strength)

VisionX
Kinetic.AI
SmartTwin
IEdgeX
Robotics, Tele‑operations, automated quality testing
NVIDIA Jetson, Omniverse, Holoscan, Metropolis integrated with HCLTech apps

D. AI‑Ops & Unified Operations

AIOps, observability, unified service management
Agentic AI platform: AIForce.Ops (GenAI + agentic automation)

E. Innovation & Accelerators

From AI Labs & COE: Robotics, GenAI, Omniverse, AI‑RAN, Innovation Labs as a Service.

HCLTech Full Lifecycle Services for Cognitive Infrastructure offering includes stages from Assessment → Advisory → Build → Modernize → Operate → Optimize

This Cognitive Infrastructure offering assessment adopts the best practices defined under the "Strategy" & "Plan" phases of Cloud Adoption Framework

"Strategy" Phase

Understand motivations - reasons for Cognitive Infrastructure adoption strategy.
Identify desired business outcomes - Establish clear solution business outcomes. Use CAF templates to align stakeholders and decision makers on deciding which outcomes to prioritize. 3 Define business justifications - project the business impact of cloud adoption strategy.

"Plan" Phase

Take inventory of client's digital estate - Catalog all workloads, applications, data sources, virtual machines, and other IT assets for migration to Azure driven AI Infrastructure
Create Migration Plan by prioritizing workloads based on business impact & technical complexity.
Document the findings showcasing the long-term transformational business benefits.

HCLTech Assessment Deliverables:

Cognitive Infrastructure solution Readiness
Target Deployment Model
Migration Complexity Assessment for moving to Cognitive AI Infrastructure
High Level Migration Plan
Design & Implementation Effort Estimates

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