The bar for AI engineering keeps moving
Shipping a demo with an LLM is easy. Shipping something that holds up at production scale, audit, and cost is the actual job.
Most teams I talk to have shipped a prototype. The interesting question isn't whether AI works for their use case — it's whether the system around the model is good enough to keep the work usable as scale, scrutiny, and cost catch up to it.
That gap is the part the studio exists to close. Evals, observability, retrieval quality, and the ergonomics of the loop a human still sits inside — those are the ingredients that decide whether a prototype graduates into a product.
The work hasn't gotten easier; the bar has just moved. Six months ago, "we wired up the model" was enough to celebrate. Today, "we wired up the model and it's still good in three months" is the actual milestone.
What stays constant is the discipline. Build the smallest thing that solves the real problem, instrument it before you scale it, and design for the team that inherits the codebase a year from now.
— Sunitha Giduturi