Secure AI platform design
Architecture work around identity propagation, isolation, observability, bounded execution, and operator intervention for live AI systems.
Consulting
Selective work around secure AI systems, governed platforms, and architecture controls
This page stays within what the source materials support: control-minded AI systems, secure platform design, cloud and Kubernetes hardening, and architecture reviews.
Governed RAG and ingestion controls
Release-gated ingestion pipelines, retrieval integrity checks, chunking and embedding policy, audit trails, and rollback-aware promotion flows.
Platform hardening for Kubernetes and cloud
RBAC, GitOps, policy enforcement, observability, and secure automation patterns for AWS, GCP, and Kubernetes environments.
Architecture reviews and control catalogs
Operator-first reviews of state transitions, retry behavior, cost controls, output handling, and failure recovery paths.
How the work is framed here
The positioning emphasizes architecture depth, policy and control design, retrieval integrity, secure infrastructure, and operator-facing resilience. It does not claim packaged products, pre-set pricing, or unsupported case studies.
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