./start
// autonomous systems, self-hosted

We build AI infrastructure that runs on your own iron.

Agent pipelines, LLM integration, and automation built to run on hardware you control — not a subscription you rent. From inference serving to full deployment automation.

./start → ./contact
agent-runner.sh
$ hyp3r deploy agent --model local
→ resolving inference backend...
→ backend online (multi-GPU, local)
# agent pipeline wired to task queue
$ hyp3r status
→ self-hosted fleet up · inference 69 tok/s · all nominal
$ _

What we build

Practical AI engineering — agents, inference infrastructure, and the automation that keeps it running.

◉

Agent Development

Autonomous and semi-autonomous agents wired into real tools and real workflows — not just chat demos.

agents
✦

LLM Integration

Wiring language models into existing systems: retrieval, tool-use, structured output, and evaluation.

llm
⚙

Infrastructure Automation

Deployment, monitoring, and recovery automated end to end — infrastructure that repairs itself before you notice.

automation
◆

Self-Hosted Inference

Local model serving on your own hardware — full control over data, cost, and latency, with no vendor lock-in.

inference
◻

Systems Design

Architecture that scales from a single box to a full multi-node lab, designed for the failure modes that actually happen.

architecture
●

Ops & Reliability

Monitoring, alerting, and incident response built in from day one — not bolted on after the first outage.

reliability

Built on

The tools underneath the agents.

Python FastAPI PyTorch llama.cpp Docker Proxmox PostgreSQL SQL Server n8n TypeScript

Latest Posts

Notes on agents, infrastructure, and everything that broke along the way.