Private AI Infrastructure: Building a Local LLM Stack for Secure Agent Development
The Challenge
Sensitive development work needed a private, cost-effective way to run local language models for prototyping, testing, and agent workflows without depending entirely on public APIs.
The Approach
We built a local AI serving stack around self-hosted inference, remote observability, and workflow automation so new agents could be tested quickly while keeping control over cost, latency, and data exposure.
What Was Built
The stack combined local model serving, telemetry forwarding, dashboarding, and scripted operational checks. It was designed to support experimentation with multiple models while keeping deployment, monitoring, and rollback straightforward.
Measurable Outcome
The result was a stable internal platform for agent development and evaluation. It reduced reliance on paid external inference for everyday testing and made model behaviour easier to observe under real workloads.