Building AI systems that actually ship
The hard part of AI products is often the system around the model: orchestration, retrieval, auth, billing, observability, storage, and repeatable workflows.
A surprising amount of AI work looks impressive for two minutes and becomes hard to trust after twenty. Shipping something useful usually means spending more time on the path around the model than the prompt itself.
That path includes input structure, retries, retrieval, storage, authentication, billing surfaces, observability, and making sure the product still behaves sensibly when model output is imperfect.
The systems worth building are usually the ones that stay useful after the demo energy disappears. That is where backend design, product clarity, and deployment discipline start to matter.