Solutions
Validated deployment guides for the problems AI platform teams keep hitting.
Every solution here starts with a named problem, not a product. Each one has been built end to end on a live cluster, every command run and every output captured from the cluster that produced it. Where the product surprised us, the guide says so. You can read a solution to understand the shape of a problem, or follow it from as tanding start and have the thing running. Both are supported. Neither requires you to talk to us first.
Consolidating, Controlling, and Optimizing MCP Tool Access on Kubernetes
Every team that gives an agent a tool stands up another server, and every client has to be told about all of them. None of those servers knows who is calling, and the whole tool catalog goes to the model on every turn. Put one controlled endpoint in front of every MCP server and REST API your agents call: tools scoped to the caller, identity that survives the hop to the backend, and a measured reduction in what each agent carries in context.
Securing, Observing, and Controlling LLM Consumption on Kubernetes
Your token spend stopped being proportional to your usage the day you deployed your first agent. Provider keys are spread across teams and repositories, spend arrives as one number a month late, and nobody can say which team accounted for it. Put a gateway between your applications and your providers: verified callers, guardrails, a price on every request attributed to a person or a team, and budgets that stop a runaway job before the provider is ever called.