A monitor showing a branch-and-merge graph with cyan bars measuring each merge, in a fintech office at night

Engineering Analytics

Fintech
Fintech

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.

Engineering analyticsAI adoptionFintechAzure DevOps

Key Challenges

Client: Large fintech company

Validation: 500+ pull requests

Scoring: Semantic task alignment, constructs, change type

Integration: Azure DevOps tasks and PRs

The project faced several key challenges:

  • Measuring without gaming: Line counts reward padding. The scoring model had to capture meaningful change and resist inflation.
  • Proving AI ROI: Correlating token spend with delivery outcomes turned 'AI helps' from opinion into a number.
  • Honest reporting: Analytics about people need care - the platform reports effort and trends, not leaderboards.

Results

  • Validated on 500+ PRs
  • Anti-gaming effort scoring
  • AI-adoption ROI measured
  • Ticket-quality insight: clearer tasks → better code

500+

pull requests used to validate the scoring model.

Task ↔ PR

semantic alignment instead of line counting.

Insight

a business-analysis bottleneck surfaced from the data.

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