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Shopify demonstrates a practical approach to optimizing AI infrastructure by post-training open-source models like NVIDIA Nemotron 3.5 Lightning for specific commerce tasks, achieving significant speed and cost reductions over frontier models. This strategy leverages an internal continual learning loop that uses automated evaluation to refine prompts and control flow before deploying specialized "task experts" on proprietary hardware. The article highlights how controlling model weights allows merchants to bypass API usage costs while maintaining high performance in context-heavy operations like GraphQL querying.
How Shopify turns open models into task experts

Sidekick, Shopify’s AI commerce assistant, runs on a frontier model that talks to the merchant, plans the work, and calls tools. For focused jobs, it hands off work to subagents, several of which run open models we’ve post-trained for that one task. Take the GraphQL subagent. When a merchant asks, “Which products are almost out of stock?”, it reads Shopify’s API schema, searches the docs, writes and runs a query, and returns the results. That work is context-heavy: the subagent reads about 40 tokens for every one it writes, and a frontier model’s API charges for every one…
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