Your AI models deserve production-grade infrastructure. We build and operate the complete ML platform stack on Kubernetes — from GPU clusters to model serving endpoints.
What We Deploy
- LLM Serving & Gateway — vLLM, TGI, Ollama on Kubernetes with LiteLLM as a unified API gateway for 100+ model providers. Cost tracking, rate limiting, and failover built in.
- ML Experiment Tracking — Langfuse for LLM observability, prompt management, and evaluation. Full trace visibility from prompt to response.
- AI Agent Infrastructure — Kubernetes-native agent orchestration, workflow engines (Argo Workflows, n8n), and agent-to-agent communication buses (NATS).
- RAG & Vector Pipelines — Vector database deployment, embedding pipelines, and retrieval infrastructure on Kubernetes with Ceph-backed object storage.
- Local LLM Inference — NVIDIA DGX Spark running Qwen models for on-premise inference. Zero API costs, full data privacy, sub-100ms latency.
- GPU Cluster Management — Node scheduling, resource quotas, multi-tenant GPU sharing, and cost allocation across teams.
Our Stack
NVIDIA DGX Spark · Qwen · LiteLLM · Langfuse · Open WebUI · vLLM · Argo Workflows · n8n · NATS · Kubernetes · Ceph Object Storage
Why Us
We run this stack ourselves. Our internal platform serves 18 AI agents across multiple LLM providers, with an NVIDIA DGX Spark for local inference running Qwen models — zero API costs, full data privacy, sub-100ms latency. Full observability via Langfuse and unified routing via LiteLLM — all on bare-metal Kubernetes.