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AI & Data

AI Infrastructure

Build the compute, storage, and networking foundation your AI program needs to scale.

AI workloads behave differently from anything your data center was designed for. GPU clusters, model training, and real-time inference push compute, storage, and networking past traditional limits.
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Enterprise infrastructure for the AI era

AI infrastructure decisions might seem familiar, but the workloads they carry are anything but. While a standard enterprise network might deliver one gigabit to the desktop, a modern AI factory needs 800 gigabits per second and is moving toward 1.6 terabits. Supply chain delays and unpredictable pricing are reshaping what’s possible and at what cost — all while mid-market and enterprise organizations are being asked to make multi-million-dollar infrastructure decisions.

Get the foundation right, and your AI program compounds. Get it wrong, and you’re funding a rebuild before the first model goes into production.  

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The CBTS approach

The full fabric, engineered together

CBTS designs and delivers the full AI fabric. In doing so, we draw on decades of enterprise compute and storage work, two of the deepest managed networking practices in our region, and our partnerships with the OEMs shaping the AI factory market.  

We meet clients across the full range of readiness — from teams that need a precise integration and fulfillment partner to those that need consultative help shaping the use case, platform, and operating model from the ground up.

AI Infrastructure capabilities

AI Infrastructure capabilities

 CBTS AI and data strategy engagements are organized around two parallel disciplines. Most
clients need both, but we shape every engagement to the entry point that makes the most
sense for your business.

AI Infrastructure

Where to start

Advisory engagements

A CBTS advisory is a time-bound, fixed-fee engagement designed to give you a clear answer to a specific strategic question — fast.  

AI & Data Maturity Assessment

Duration: Four weeks

Best for organizations that want a clear, third-party read on where they stand on AI and data readiness and where to focus first.

You walk away with: 


  • Current-state assessment across both AI and data dimensions
  • Gap analysis against industry benchmarks and your own stated AI ambitions
  • Prioritized list of foundational gaps to close before scaling AI investment
  • Short-form executive readout deck for leadership alignment
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What success looks like

Your AI infrastructure investments should reshape what your organization can build, ship, and scale. Three outcomes show up most frequently in the AI factories we deliver.

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Business agility

AI workloads that used to take weeks of provisioning and capacity planning now move in days or hours. Instead of being the rate limiter on the AI program, infrastructure becomes the platform it accelerates against.

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Improve productivity

Enterprise AI infrastructure enables your teams to apply AI to work they couldn’t before. As a result, you can close skills gap, compress cycles, and free capacity to focus on growth and innovation.

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Revenue growth

Tap into faster model training, faster inference, and faster time to market for AI-powered products and features. Early infrastructure decisions shape how quickly AI investment translates into new revenue.

In practice

1.6Tbps interconnect speeds engineering into AI factory networking fabric
24x7 managed operations across hybrid AI infrastructure environments
Partner validated AI-ready platforms from NVIDIA, Cisco, HPE, Dell, and Lenovo

Don’t take our word for it

“I love the creative, tailored solutions that are delivered in a consistent and reliable way while always doing what it takes to make things right.”

Chief Technology and Information Security OfficerFinancial Services / Banking

“My team at CBTS have been trusted partners for a long time. They provide excellent technical support and pre-sales work. Their breadth of knowledge and ability to bring in the right resources have helped us steer our technology into the future.”

Managing Director, CISO, Head of TechnologyPrivate Equity / Financial Services

“CBTS treats us like a partner and not just a customer. The technical expertise is next to none and the relationship management is some of the best I have experienced.”

Director, Telecom and Architecture ServicesHealthcare

Infrastructure is one layer of the AI program

AI infrastructure is the foundation but not the whole program. Most CBTS clients engage across several of the disciplines below as their AI program matures.

Related insights 

Frequently asked questions 

What is AI infrastructure? AI infrastructure, also known as an AI factory, is the compute, storage, and networking foundation AI workloads run on. Unlike traditional enterprise infrastructure, it’s designed around the demands of GPUs, cloud platforms, and data pipelines.
What’s the difference between AI infrastructure and traditional IT infrastructure? Traditional IT infrastructure was built around general-purpose CPUs, standard enterprise networking, and storage optimized for transactional systems and end-user applications. AI infrastructure adds GPUs, dramatically higher networking bandwidth — often 800 Gbps moving toward 1.6 Tbps — and storage tuned for the throughput training and inference demand. Other differences include the power and cooling profile, cost profile, and architectural assumptions about how compute, storage, and networking interact. Most enterprises end up running both side by side, which is why hybrid AI infrastructure design has become central to the conversation.
Do we need on-premises AI infrastructure, or can we run everything in the cloud? It depends on the workload. Some AI use cases run well in the public cloud, where GPU capacity can be rented on demand and capital expense is avoided. Others, such as sustained training workloads, latency-sensitive inference, workloads with strict data residency or sovereignty requirements, make a stronger case for dedicated on-premises or colocated infrastructure. Most enterprise AI programs end up hybrid by design, with workloads in the cloud, on-premises, and at the edge. The right mix depends on cost, performance, data gravity, and regulatory constraints. CBTS designs hybrid cloud AI infrastructure with all four factors in view from the start.
Why do so many AI projects fail, and is infrastructure the reason? Industry research consistently puts the AI project failure rate above 70 percent, with some studies citing 80 percent or higher. Infrastructure is rarely the primary cause. The more common reason is data. AI projects fail because the underlying data isn’t structured, governed, or accessible in a way the AI can use. That said, infrastructure failures show up in their own way, including undersized GPU capacity, networking bottlenecks, storage that can’t keep pace, or a hybrid design that drives costs out of control. CBTS addresses both sides of the foundation, which is why our AI infrastructure work is tied closely to our data engineering and governance practices.
How does CBTS work with partners like NVIDIA, Cisco, HPE, Dell, and Lenovo? CBTS sits inside the design and build conversations with our OEM partners rather than outside placing orders. Our architects work directly with NVIDIA on validated AI factory designs, with Cisco on AI-ready networking and pod architectures, and with HPE, Dell, and Lenovo on the compute and storage platforms underneath. For clients with sophisticated engineering teams, we operate as a precision integration and fulfillment partner; for clients earlier in their AI journey, we lead the consultative design work and bring the right partner platforms to the table based on the use cases, not the other way around.

Strong foundations start with strategy

The infrastructure decisions you make in the next 12 months will shape what your organization can build for the next decade. We’re the partner who can help you make those choices with confidence.