Thanks! We actually pivoted to working on this problem at the beginning of last year. We were previously called Syndetic and were building software for data pro

Thanks! We actually pivoted to working on this problem at the beginning of last year. We were previously called Syndetic and were building software for data providers. As we considered pivoting I spent some time thinking about the biggest problems I've faced maintaining software and started doing personal consulting. I upgraded a bunch of Rails and React apps for various companies over the course of 2022. Toward the middle of last year I re-connected with Andy (our third cofounder) who I went to school with and have wanted to work on a company with for a long time. We did a bunch of customer discovery / product work to figure out how to take what I learned doing this by hand and turn it into a software product. That was exciting enough that we decided to bring Andy on as a third co-founder and pivot our YC company. Allison and my background is in building data businesses. Before Infield and Syndetic we worked at a startup in the beverage industry where we standardized inventory data for every alcohol product sold in the US. As we got into building Infield we didn't expect to use LLMs at all. We imagined a similar human-in-the-loop expert system to what we've built before. I've been extremely impressed with recent language model's ability to handle unstructured changelog text. For example, consider the following snippet of a changelog: Security: - Address an issue with password validation Breaking change: - The `foo?` method now returns a boolean instead of int Language models can carry the context through, so we are able to not just parse this apart into discrete changes (which I could figure out how to do with a regex) but also bring in context and categorize them. It can do this generically and really feels like something new.