Custom-designed, self-hosted,
production-ready.
We design, deploy, and optimize end-to-end AI systems — from LLM inference pipelines and voice processing to workflow automation and real-time monitoring.
What We Design
LLM Inference & Serving
Deploy and optimize local LLM pipelines with vLLM, llama.cpp, and TensorRT — tuned for your GPU topology and throughput targets.
AI Infrastructure
Self-hosted stacks on Docker Compose or K3s — model gateways, monitoring, CI/CD, and infrastructure-as-code you can actually maintain.
Voice Pipelines
End-to-end speech-to-text and text-to-speech with Speaches, Wyoming, Kokoro, and Whisper — low latency, self-hosted, GPU-accelerated.
Workflow Automation
n8n workflows, Litellm proxy customization, and Firecrawl integration — connect your tools, automate the noise, and surface what matters.
Monitoring & Observability
Prometheus, Grafana, LiteLLM admin UI, and custom healthchecks — know your infra state before users feel it.
Strategy & Architecture
Model selection, hardware sizing, topology planning, and migration strategy — from single-GPU setups to multi-node clusters.
Built on infrastructure, not opinions
We treat AI infrastructure the same way engineering teams treat production environments: observable, controllable, and maintainable. Every stack we build runs on hardware we own — no vendor lock-in, no black boxes.
We run our own DGX Spark, Proxmox clusters, K3s, and TrueNAS systems — so when we recommend an architecture, it's been battle-tested across real deployments, not just read from documentation.