AI Engineering
Moving AI from Prototype to Production
Most AI projects stall between demo and production. The gap isn't the model—it's the platform.
Read insight →Thinking on AI platforms, DevOps practices, cloud architecture, and the engineering decisions that matter when building production-grade systems.
Most AI projects stall between demo and production. The gap isn't the model—it's the platform.
Read insight →Platform engineering is not DevOps with a new name. It's a shift from managing infrastructure to building internal products.
Read insight →Writing Terraform is the easy part. The hard part is module architecture, state management, and CI/CD at scale.
Read insight →Practical patterns for indexing, retrieval, context management, and evaluation in production RAG systems.
Read insight →Real observability means answering novel questions about system behavior without deploying new instrumentation.
Read insight →Evaluation frameworks, guardrails, and audit trails that make AI safe to run in production.
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