Built algorithms that put apples-to-apples labels on school budget line items so that districts understand how their spending stacks up and where they can improve, saving months of manual processing each year.

Approaches include: Natural language processing (NLP), machine learning challenge, Excel tooling, ranked prioritization for manual follow-up