AI Platform & Infrastructure

AI workloads have particular demands: GPUs, fast storage and predictable networking. Our engineers design and build the infrastructure to run them, on cloud or on-premises, sized to your usage and costed so the platform scales with demand without surprising you on the monthly bill.

Frameworks and tooling

We set up the frameworks AI applications depend on and integrate them with your data, identity and deployment pipeline. Your engineers inherit a foundation with defaults for cost, security and observability already in place.

Model hosting

Open-weight models you run yourself, commercial models you proxy, or both. Hosting keeps control of your data and your costs in one place, with scaling, access control and observability attached, so teams call a stable endpoint instead of each rebuilding the plumbing.

LLMOps and observability

The work continues after a model reaches production. The operations layer covers prompt and version management, evaluation, monitoring and cost tracking. That gives you a clear view of quality and spend over time, and the freedom to change models or prompts without breaking what already works.