Production AI starts with infrastructure that's built for it.
Our reference architectures combine GPU and accelerator pools, low-latency data fabrics, vector stores, and MLOps automation. You ship models — we make sure the platform underneath is reproducible, governed, and observable.
Capabilities included.
Accelerator platforms
GPU clusters with Kubernetes, scheduling, and quota tooling tuned for training and inference.
Data fabrics & feature stores
Streaming, lakehouse, and vector layers wired into your existing systems of record.
MLOps
Pipelines from notebook to canary in production — with lineage, evaluation, and rollback.
Agentic AI runtimes
Powered by DAIRO, our control plane for orchestrating agents across hybrid cloud.
Measurable impact, every quarter.
Frequently asked questions
What is AI-ready infrastructure?
It's the GPU-class compute, high-throughput data fabrics, and MLOps tooling that production AI and ML workloads need — engineered for scale, utilization, and reliability rather than one-off experiments.
Do we need GPUs to run AI in production?
For training and many inference workloads, yes — but utilization matters more than raw count. We design platforms that schedule and share accelerators efficiently across teams to maximize ROI.
Can you build AI infrastructure across hybrid and multi-cloud?
Yes. We deploy AI-ready platforms in your data center, in public cloud, or both, with a consistent MLOps layer across them.
How does this relate to agentic AI?
AI-ready infrastructure is the foundation agents run on. Pair it with our Agentic AI Services to take models from platform to production agents.