Engineering Analytics
A large fintech company needed a repeatable way to measure engineering effort, track individual progress and quantify whether AI tooling improved delivery. RemSoft.Dev built and deployed an AI engineering analytics platform, validated on more than 500 pull requests. It links PRs to their source tasks and scores real engineering effort through semantic task alignment, code constructs and change type - not raw line counts - so inflated pull requests cannot distort the result. The system tracks developer output over time, quantifies AI adoption by correlating token spend with delivery speed, completed tasks and defects, and measures ticket quality. One finding changed how the company worked: the data exposed a business-analysis bottleneck - clearer tasks consistently produced better code.