  We study the efficacy of Small Language Models (SLMs) in facilitating
application usage through natural language interactions. Our focus here is on a
particular internal application used in Microsoft for cloud supply chain
fulfilment. Our experiments show that small models can outperform much larger
ones in terms of both accuracy and running time, even when fine-tuned on small
datasets. Alongside these results, we also highlight SLM-based system design
considerations.
