A production-style platform for deploying and operating AI applications on Kubernetes, with infrastructure as code, GitOps delivery, observability, autoscaling, and reliability controls.

• ·Infrastructure as code
• ·Kubernetes application architecture
• ·GitOps deployments
• ·Secrets and configuration
• ·Autoscaling
• ·Observability
• ·Failure recovery
• ·SLOs
• ·Infrastructure cost

An incident-analysis system that collects Kubernetes logs, extracts relevant failure evidence, and generates structured incident reports containing likely root causes, affected components, and remediation suggestions.

• ·Log collection and noise reduction
• ·Failure evidence extraction
• ·Schema-validated LLM output
• ·Structured incident reports
• ·Human-reviewable remediation

An OpenAI-compatible inference gateway exploring request routing, rate limiting, caching, autoscaling, observability, and the operational characteristics of serving AI workloads.

• ·OpenAI-compatible API
• ·Authentication and rate limiting
• ·Request routing and queues
• ·Semantic caching
• ·Autoscaling on queue depth
• ·Latency and token throughput
• ·Cost visibility

A controlled environment for creating infrastructure failures and observing how applications and monitoring systems respond.

• ·Pod termination
• ·Resource exhaustion
• ·Broken dependencies
• ·Node failure
• ·Increased network latency
• ·Bad deployments
• ·Application crashes