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