The Challenge
A seemingly simple feature request revealed deep complexity lurking in the codebase.
Implement Secure Ticket Deletion
The QEEK platform needed a complete ticket deletion feature with proper authorization, cascading deletes, and backend security. Initial analysis estimated this would take 9-10 days of development work.
Critical Bug Discovered
Codebase had duplicate implementations using both Firestore AND Realtime Database
No Backend API
All ticket operations were client-side only — major security vulnerability
Complex Cascading Logic
Linked tickets, atomic children, and parent references needed cleanup
Authorization Requirements
Role-based permissions needed for ticket creators, admins, and regular members
The QEEK-Powered Solution
Two tools working in perfect harmony — QEEK provides the understanding, AI provides the execution.
QEEK Analysis
Deep code understanding
Instantly identified database inconsistency
Found duplicate Firestore + Realtime DB usage
Mapped all affected files
6 files across services, hooks, and components
Verified drag-and-drop integrity
Confirmed no breaking changes to existing UX
Detected orphaned dead code
Identified deprecated methods to remove
AI Execution
Powered by QEEK context
Created secure backend API
Firebase Function with role-based auth
Updated 6 files simultaneously
Services, hooks, components, and types
Added UI with error handling
Delete buttons + confirmation modals
Deployed to production
Zero bugs, first attempt
What Was Delivered
Secure Backend API
Firebase Function with role-based authorization
Cascading Deletes
Proper cleanup of linked tickets and children
Frontend Integration
Updated services, hooks, and components
UI Components
Delete buttons with confirmation modals
Critical Bug Fix
Resolved database inconsistency issue
Error Handling
Comprehensive user feedback throughout
The Results
productivity improvement
Traditional Approach
Estimated timeline
- Manual code exploration
- Risk of missing edge cases
- Database bug might go unnoticed
- Multiple rounds of testing
- Context switching overhead
QEEK + AI Approach
Actual time
- Instant codebase understanding
- All edge cases identified
- Critical bug discovered & fixed
- Production-ready on first deploy
- Zero context switching
Key Takeaways
Lessons learned from achieving 100x productivity on a real-world feature.
Context is Everything
QEEK's deep understanding of the entire codebase enabled it to spot a critical database inconsistency that would have caused production issues. Traditional development might have missed this entirely.
Speed Without Compromise
The 100x speed improvement didn't come at the cost of quality. The implementation included proper authorization, error handling, cascading deletes, and comprehensive logging — all production-ready.
The Future of Development
This isn't about replacing developers — it's about augmenting them. Developers focus on product vision and architecture while AI handles the implementation details with perfect accuracy.
"What would have taken over a week was done in 10 minutes. QEEK didn't just save time — it found a critical bug we would have shipped to production."
Ready to 100x your productivity?
See how QEEK can transform your development workflow — from days to minutes.