Vendor-Neutral. Workload-First.

The GPU hardware layer that sets the performance envelope for training and inference. We design around the characteristics of current-generation accelerators from both major vendors, selecting per workload rather than per allegiance.

NVIDIA

GPU families and the CUDA software stack for training and inference across datacenter and edge deployments.

AMD

Instinct-class accelerators and the ROCm software ecosystem for training and inference workloads.

Public cloud platforms provide elastic AI capacity, managed services and global reach. We are vendor-neutral: platform selection follows workload profile, data gravity, cost structure and existing enterprise commitments.

AWS

GPU compute options, managed AI services and landing-zone patterns for enterprise AI estates.

Microsoft Azure

GPU capacity, Azure AI services and hybrid-consistent identity and governance tooling.

Google Cloud

GPU and TPU capacity, managed AI services and data platform integration.

The scheduling layer decides how capacity is shared, how jobs land on topology, and how multi-tenancy is enforced. We work across the Kubernetes ecosystem and HPC-native schedulers, choosing per estate.

Kubernetes

GPU scheduling, device plugins, autoscaling and multi-tenancy for serving and training platforms.

OpenShift

Enterprise Kubernetes distributions with integrated security and operations tooling.

Slurm

HPC-grade scheduling for research and large-scale training estates, including fair-share and priority policies.

The software layer where models are trained, adapted and served. Selection follows workload characteristics: throughput, latency targets, memory footprint and operational maturity.

PyTorch

The dominant training and research framework, including distributed training tooling.

Hugging Face

Model and dataset ecosystems, fine-tuning tooling and open-model distribution.

Model-serving runtimes

High-throughput LLM serving stacks with continuous batching, paged KV-cache and quantization support.

Infrastructure-as-code is how AI platforms stay reproducible, reviewable and auditable. We codify environments end to end, from landing zones to cluster configuration.

Terraform

Declarative provisioning of cloud and on-premises infrastructure with environment parity.

Configuration automation

Cluster and node configuration management integrated with IaC pipelines.

CI/CD

Pipelines for infrastructure, applications and model deployment with automated evaluation gates.

AI systems demand observability across infrastructure, serving behavior and model quality. The lifecycle tooling ties development to production with versioning, evaluation and rollback.

Metrics, logs & tracing

Unified telemetry across GPU health, serving latency and application behavior.

Model registries & versioning

Lineage for models, datasets and configurations with rollback paths.

Evaluation pipelines

Automated quality gates that make model changes measurable and reversible.