LLM & AI Infrastructure Development

We support every part of the infrastructure stack needed to run modern AI, from GPU cluster design to training pipelines and deployment environments. Whether you’re building domain specific LLMs, high throughput inference systems, or simulation models, we help ensure your stack is efficient, maintainable, and properly scaled.

We work with clients who need more than just capacity, they need systems that are mapped to real performance goals, energy constraints, and operational requirements. That includes open-weight models, sovereign AI environments, and multi-site coordination.

What we do

Build and support the full AI lifecycle, from hardware to output.

We handle physical infrastructure, workload planning, training environments, and deployment architecture. We don’t overbuild. We design what fits your actual needs and workloads—then help you scale or refine as usage grows.

Whether you’re just starting to explore AI integration or deploying frontier models, we provide the technical depth and structured execution to move your systems forward.

Services Offered

Targeted solutions that improve how you work

We offer the full stack of tools and infrastructure needed to train, fine-tune, and deploy large-scale AI models. Our focus is on building environments that are stable during long runs, transparent during debugging, and efficient enough to scale without waste. Whether you’re training a foundational model or refining a small one for specific use, we support every step of the technical process, from setting up the cluster to running your final checkpoints.

GPU Cluster Design for Training Loads

Layouts and specs based on training scale, hardware mix (GB200, MI300, etc.), and growth planning

GPU Cluster Design for Training Loads

Layouts and specs based on training scale, hardware mix (GB200, MI300, etc.), and growth planning

GPU Cluster Design for Training Loads

Layouts and specs based on training scale, hardware mix (GB200, MI300, etc.), and growth planning

Model Training Environment Setup

Tooling and architecture for running large-scale training, including checkpointing, logging, scheduling, and rollback

Model Training Environment Setup

Tooling and architecture for running large-scale training, including checkpointing, logging, scheduling, and rollback

Model Training Environment Setup

Tooling and architecture for running large-scale training, including checkpointing, logging, scheduling, and rollback

Fine-Tuning Infrastructure

Structured support for smaller domain specific training runs, with dataset handling and multiple configuration paths

Fine-Tuning Infrastructure

Structured support for smaller domain specific training runs, with dataset handling and multiple configuration paths

Fine-Tuning Infrastructure

Structured support for smaller domain specific training runs, with dataset handling and multiple configuration paths

Distributed Training Optimization

Support for multi-node orchestration, data sharding, and networking bottlenecks across high throughput clusters

Distributed Training Optimization

Support for multi-node orchestration, data sharding, and networking bottlenecks across high throughput clusters

Distributed Training Optimization

Support for multi-node orchestration, data sharding, and networking bottlenecks across high throughput clusters

Profiling & Debugging Tools

Real time metrics to identify stuck gradients, underperforming nodes, or inefficiencies in training workflows

Profiling & Debugging Tools

Real time metrics to identify stuck gradients, underperforming nodes, or inefficiencies in training workflows

Profiling & Debugging Tools

Real time metrics to identify stuck gradients, underperforming nodes, or inefficiencies in training workflows

Sovereign & Open-Weight Model Hosting

Fully isolated or nationally compliant training environments, with storage, compute, and data governance control.

Sovereign & Open-Weight Model Hosting

Fully isolated or nationally compliant training environments, with storage, compute, and data governance control.

Sovereign & Open-Weight Model Hosting

Fully isolated or nationally compliant training environments, with storage, compute, and data governance control.

How we work

Structured, data-driven, and practical

Our process is built to support real-world AI workloads, not just theory. We take time to understand your use case, match it with the right compute setup, and make sure the environment holds up across iterations. Each step is structured to reduce friction and make the system easier to operate, debug, and scale.

Define the Training Scope

We align around the model you’re building, what data it needs, and how long it will take, then match that to a suitable setup

Define the Training Scope

We align around the model you’re building, what data it needs, and how long it will take, then match that to a suitable setup

Define the Training Scope

We align around the model you’re building, what data it needs, and how long it will take, then match that to a suitable setup

Build the Environment

We set up the clusters, file systems, monitoring, and dependencies needed to start training and keep it running smoothly

Build the Environment

We set up the clusters, file systems, monitoring, and dependencies needed to start training and keep it running smoothly

Build the Environment

We set up the clusters, file systems, monitoring, and dependencies needed to start training and keep it running smoothly

Test & Launch

We validate your environment before the first run, making sure data moves cleanly, logs work, and compute resources are balanced

Test & Launch

We validate your environment before the first run, making sure data moves cleanly, logs work, and compute resources are balanced

Test & Launch

We validate your environment before the first run, making sure data moves cleanly, logs work, and compute resources are balanced

Monitor, Iterate, Support

We help track results, fix issues as they come up, and adapt the system over time, whether that means scaling, switching models, or optimizing batch cycles

Monitor, Iterate, Support

We help track results, fix issues as they come up, and adapt the system over time, whether that means scaling, switching models, or optimizing batch cycles

Monitor, Iterate, Support

We help track results, fix issues as they come up, and adapt the system over time, whether that means scaling, switching models, or optimizing batch cycles