We help engineering teams build the infrastructure required to train, fine-tune, evaluate, deploy and operate AI models in production. This is the platform layer underneath serious AI systems, not another isolated prototype.
What we deliver
- Distributed model training
- LLM fine-tuning and post-training pipelines
- Kubeflow and Ray-based AI platforms
- Kubernetes GPU workload scheduling
- Experiment tracking and model lifecycle
- Data and training pipelines
- Model serving and inference architecture
- Evaluation, monitoring and LLMOps/MLOps
- Cost, performance and reliability engineering
How we work
- Assess. We review your AI workloads, data flow, infrastructure, bottlenecks and production requirements.
- Pilot. We build one real training, fine-tuning or inference workload end to end.
- Build. We turn the validated architecture into a production platform your team can operate and extend.
Frequently asked questions
Do you only build AI applications?
No. We focus on the infrastructure and engineering layer behind production AI: training pipelines, fine-tuning systems, inference, evaluation, observability and Kubernetes/GPU platforms.
What technologies do you work with?
Depending on the workload, we work with tools such as Kubernetes, Kubeflow, Ray, PyTorch, Hugging Face, vLLM, Terraform, GitOps and cloud or private GPU environments.
What is the best first step?
For most teams, the best first step is an AI Platform Assessment. We identify the current gaps, the right architecture and the fastest path to a production-ready platform.
Ready to start? Start with an AI Platform Assessment or book a scoping call.