> forward_deployed
LLMOps
LLMOps is everything that keeps an AI system alive after the demo: how it ships, how it is guarded, how tenants are isolated, and how changes reach production without breaking a customer's Tuesday.
> what this looks like in practice
▸CI-integrated delivery on the AI-Infused QE Platform: Dockerized services on Kubernetes behind a load balancer, shipped through CI/CD pipelines into customer-managed AWS, Azure, and GCP, with the platform's own agentic test automation running in those same pipelines.
▸Guardrails as infrastructure, not prompts: input scoping, output grounding checks, and per-tenant policy configuration live in the serving layer where they cannot be prompt-injected away.
▸Tenant-aware RBAC with module-level CRUD-X permissions and role templates, so a new enterprise customer onboards without a rebuilt permission model.
▸Custom MCP integration as an operational surface: the Playwright MCP server that turns natural language into production-grade Playwright and Cypress scripts is versioned, tested, and shipped like any other service, cutting test authoring time ~75%.
▸Agent-guardrail engineering in WealthOS: policy hooks, frozen-path protection, and specialist review agents that let agentic coding tools build under enforced constraints.
> where it shows up
The AI-Infused QE Platform (the full production spine), Loop Copilot (Railway CI/CD, Entra ID auth), and WealthOS (guardrail harness).