Case Studies
Deep-dives into backend architectures, performance metrics, and design schemas of production-ready systems.
AI-Powered Support Ticketing System
High-performance real-time customer support platform with automated resolution suggestions
An enterprise-grade support ticketing system supporting multi-tenancy. It dynamically processes incoming client requests, streams real-time updates to staff dashboards, and integrates Gemini AI to auto-draft suggested email replies.
Technologies Used
Key Features
- Real-time updates to agent queues via WebSocket connections, minimizing database polling.
- Automated initial issue triage and resolution suggestions powered by Gemini Pro API.
- Multi-tenant logical database isolation, allowing secure storage of proprietary ticket details per corporate account.
- Granular Role-Based Access Control (RBAC) separating admins, managers, and support agents.
Performance Metrics
< 15ms
Socket Broadcast latency
3.4s avg
AI Resolution drafting
45ms (p95)
API Response Time
System Architecture Flow
1. Client Connection
A customer opens a ticket or logs in. A stateful WebSocket connection is established to the Express backend.
2. Task Queuing
Incoming ticket payload is validated and written to PostgreSQL. An event is dispatched to Redis Pub/Sub.
3. Gemini AI Analysis
A worker process reads the ticket and invokes the Gemini API to analyze sentiments and draft suggested answers.
4. Real-time Delivery
The drafted suggestions and ticket updates are pushed immediately to the agent console via WebSockets.
Credit Card Management System
Secure ledger database for managing credit card transactions and bill reminders
A financial platform built to help users manage credit card profiles, track daily transactions, calculate monthly outstanding billing statements, and configure payment reminders.
Technologies Used
Key Features
- Consolidated credit card ledger database storing card types, limits, statements, and transactions.
- Automated cron job scheduling utilizing Redis and BullMQ to send statements and payment alerts.
- Secure transaction logging with database transaction rollback checks, preventing race conditions.
- Planned integration of an AI chatbot for context-aware financial query processing and expense analysis.
Performance Metrics
100% ACID
Ledger transaction audit
99.98%
Reminder dispatch reliability
12ms
Query execution time
System Architecture Flow
1. Transaction Log
User transactions are submitted and written to PostgreSQL inside a secure ACID transaction scope.
2. Cron Alert Engine
Daily scheduler scans upcoming payments and queues warning payloads in BullMQ.
3. Dispatch Queue
BullMQ workers process tasks and trigger notification gateways to send reminders.