Helen Zeng

When we started, we didn’t have a useful public API at all, so the obvious move was to expose a few endpoints for our popular features: one to remove filler words, one to add captions, one to apply Studio Sound, and so on. We almost did that. But instead, we decided to consolidate the core of our API on a single agentic endpoint: Underlord, the editing agent inside Descript. There were practical reasons to start here.

Besides Azure portal, you can also do document intelligence and index creation in ML studio. The entire process of index creation includes several steps, crack_and_chunk, generate_embeddings, update_index, and register_index. In Azure ML studio you can create or use components for each of those steps and stitch them together as a pipeline. Section 1. What is it? Usually, a ML pipeline component does the job in serial, for example, it crack_and_chunk each input file, i.e., pdf file, one by one.