We design, build, and operate self-hosted LLM serving platforms, intelligent surveillance systems, and agentic RAG pipelines on Kubernetes. From GPU clusters to real-time inference.
From infrastructure provisioning to model deployment to intelligent applications. We handle the entire stack.
Production vLLM deployments on GKE and OpenShift with GPU autoscaling, model caching, and OpenAI-compatible APIs.
Retrieval-augmented generation with tool-calling agents, vector databases, persistent memory, and real-time data pipelines.
Computer vision surveillance with real-time object detection, threat classification, and natural language querying over security footage.
Every deployment is reproducible, version-controlled, and built for scale from day one.
Evaluate your workloads, GPU requirements, and model selection to design the right architecture.
Terraform, Kubernetes manifests, Helm charts, Dockerfiles. Production-ready from the first commit.
GPU node pools, model serving, vector databases, and application containers. All orchestrated on K8s.
Monitoring, autoscaling, cost optimization. Your AI infrastructure runs reliably at any scale.
Full-stack migration of a physical security surveillance system to self-hosted Llama 3.1 on Kubernetes with PGVector, GPU inference, and an agentic RAG pipeline.
Read the Case Study →From proof-of-concept to production GPU clusters. Let's build your AI infrastructure together.
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