The production gap in enterprise AI
Why promising prototypes stall after approval, and how teams can design for ownership, evaluation, and change from the start.
Insights
Notes from the production layer.
Practical writing about the architecture, operating decisions, and team structures behind dependable AI systems.
Designing retrieval systems teams can operate
Retrieval quality depends on content ownership, observability, and feedback loops as much as embedding models or vector stores.
When an AI prototype needs a platform team
The signals that a successful experiment now needs shared infrastructure, operating standards, and dedicated ownership.
Turn the next hard question into a working system.
Tell us what needs to become dependable, and where the current system gets in the way.