Engineering Control Tower
CollabAI
An engineering operations system for tracking repositories, pull requests, QA progress, and automation opportunities across active workstreams.
Context
CollabAI was built to give engineering and QA stakeholders a clearer operational view across many active repositories. The goal was to reduce fragmented tracking, manual follow-up, and hidden bottlenecks by bringing visibility and automation into one control layer.
600+
repositories tracked
5 hrs/week
manual tracking saved per week
Stack
Case Study Overview
Designed to centralize code health, review flow, and engineering visibility across many repositories.
Used automation and agent workflows to reduce repetitive project tracking and QA coordination.
Showcases product thinking beyond CRUD by combining APIs, workflow orchestration, and AI-assisted operations.
What I Built
GitHub Analytics
Architected GitHub activity tracking and reporting to monitor developer output across daily, weekly, and monthly views, giving teams better visibility into engineering activity.
Automated QA Pipelines
Built PR review and QA tracking workflows across 600+ repositories, saving project managers around 5 hours each week that would otherwise be spent on manual follow-up.
Workflow Automation
Implemented task assignment flows that triggered when bugs or delivery bottlenecks were identified during PR review, helping move issues into action faster.
Multi-Channel Notifications
Integrated Slack and email notification paths to keep stakeholders updated in real time on QA status, review findings, and assigned next steps.
Knowledge Base Integration
Connected code quality and reporting signals into a central knowledge layer so project information became easier to search, understand, and act on.
Outcomes
Centralized repo-level engineering visibility across a large active surface area.
Reduced manual QA/project tracking overhead for project managers.
Turned review signals into automated follow-up workflows instead of passive reporting.
Why This Matters
This project highlights systems thinking across analytics, workflow orchestration, notifications, and AI-assisted operations, showing how engineering tools can reduce management overhead instead of just displaying data.