AI infrastructure
AI on infrastructure you own and control. One team designs it, builds it and runs it.
We design, build and run the platform your AI sits on: GPUs and models on your own infrastructure, one gateway for every model, Kubernetes and pipelines.
AI needs a foundation most companies don't have yet
Keeping AI and data in-house means GPUs and model weights, inference engines, a gateway, Kubernetes and pipelines. All of it sits on systems and data that were never built for it. Assemble that piece by piece from different vendors and you get gaps nobody owns.
We advise, implement and manage.
- 01Advise
Platform assessment & design
We look at your use cases, your rules and the systems you already have, then design the platform they need.
- On-premise, private cloud or hybrid: what fits your data and rules
- GPU and capacity sizing for your models and volumes
- Choice of models and inference engines
- Security and access control, and data residency
- A platform roadmap: what to build first and how it grows
- 02Implement
Build the platform
We set it up on your infrastructure with our own software and connect it to what you already run.
- GPU servers and model deployment for every team
- One gateway in front of every model, local or cloud
- Kubernetes clusters and applications you can see and manage
- GitOps and pipelines, so changes are safe and repeatable
- Connections to the systems and data your AI needs
- 03Manage
Run it for you
After go-live we keep the platform healthy and current, and keep its costs in check. You get a named contact.
- Monitoring and updates, and capacity planning
- Model upgrades and cost control
- Incident response with a named contact
- Regular reviews of usage, cost and the roadmap
What you walk away with.
Your AI, your rules
Models and data stay on infrastructure you control. In the UAE, when that matters.
One platform, not a patchwork
GPUs, models and the gateway, clusters and pipelines: parts that fit together, run by one team.
Ready to build on
A foundation your developers and our engineers can start building AI applications on straight away.
Our own tools, built for this.
FastLLM Proxy
An OpenAI-compatible gateway in front of your own LLM servers and 80 hosted providers. Routing, budgets and access control live in one place.
Learn moreAgent, model and GPUaaS platformKuvryn AI
The platform for running AI agents, models and GPUs as a service. Teams run their agents and models on your own GPU servers, each in its own space, all from one console.
Learn moreAI application platformKuvryn
A self-hosted platform for AI applications on Kubernetes. See each application with its parts and its health, and when something breaks, the reason why.
Learn moreAI, automation and CI/CD/CT platformDhole
One workflow engine for build pipelines and infrastructure automation that also orchestrates AI agents, with a person approving where it matters.
Learn moreGitOpsSolder
A lightweight Kubernetes operator that keeps clusters in line with Git. It handles approvals and drift detection, and rolls back on its own. No database, no queue, no extra UI to run.
Learn moreQuestions
Can this run fully on-premise?
Yes. It runs on your own servers, including sites with no internet access, or in a private cloud you choose.
Which hardware do you work with?
Your existing servers where we can. If you need new GPU capacity, we size it and advise on hardware for your models and volumes.
Can you run it for us after go-live?
Yes. That is the Manage step: monitoring, updates, capacity and support, with a named contact.
What does it cost?
It depends on the size of the platform and how much of it we run for you. After a first conversation we propose a scope and a price.
Find out where to start.
Tell us where you are today. We’ll come back with where AI will pay off and what to do first.