Talk to an expert
Enterprise data centre infrastructure

AI INFRASTRUCTURE

Infrastructure engineered for AI.

Connect GPU compute, storage, networking and facilities into a workable platform.

Discuss your project

ENGINEERING PERSPECTIVE

Architecture that stands up in practice.

Training and inference impose different demands on compute, memory, network and storage. We size from workload requirements, review facility constraints and design separate management and data paths. Operational acceptance covers telemetry, access control and recovery.

Serving organizations across India, including Punjab, Ludhiana, Mohali and Chandigarh.

WHERE IT FITS

On-premises GPU clusters, hybrid AI platforms and production inference services.

Training and inference
GPU cluster sizing
Storage and data pipelines
High-speed network fabrics
Rack power and cooling
Security and observability
REFERENCE ARCHITECTURECOMPUTE + FABRIC
GPU compute
GPU compute
Storage
Leaf fabricCapacity · congestion · resilience
Spine layer
Peer leafs
Management

Conceptual leaf-spine connectivity. Compute, storage and management paths require workload-specific design.

WHAT THE ENGAGEMENT PRODUCES

Decisions made clear.
Delivery made practical.

01

Workload and capacity assessment

02

Compute, fabric and storage architecture

03

Deployment and operational readiness plan

HOW WE ENGAGE

From assessment
to operational handover.

We agree scope and acceptance criteria, validate the design in a pilot, control the implementation and document the environment for the people who run it.

01

Understand

Review the current environment and business priorities.

02

Design & validate

Resolve dependencies, test assumptions and plan change.

03

Deliver & hand over

Implement the agreed scope, validate outcomes and transfer knowledge.

CONNECTED EXPERTISE

Think beyond one platform.

YOUR NEXT CHAPTER

Let’s build
what comes next.

Bring us your infrastructure challenge.
We’ll start with the right questions.

Talk to an expert