Casestudy
How AI agents eliminate manual work in minutes
Transform your operations with intelligent agents that handle complexity, ensure compliance, and drive measurable business outcomes.
Scalable
System
Improved
Performance
The challenge with traditional AI
Developing a Go-based microservices backend to work in tandem with a new React.js frontend.
Ensuring seamless integration into the existing SaaS application without disrupting current operations.
Establishing a replicated database environment for analytical queries.
Challenge
Integration with Legacy Systems:
The integration posed significant challenges due to the different technologies in play. These were overcome by creating a robust API layer that allowed for smooth communication between the old and new systems.
Performance Optimization:
Ensuring that the new backend services could handle the load and complexity of analytical queries without latency issues. This was achieved through careful service design and load testing.
Seamless Deployment:
The deployment had to occur without downtime. We utilized blue-green deployment strategies to ensure a smooth transition.
Solution
Technology Stack:
Go (Golang):
Chosen for its efficiency in concurrent processes and optimal performance in microservices architecture.
Docker & Kubernetes:
For containerization and orchestration, enabling scalable and resilient deployment.
gRPC & Protocol Buffers:
For efficient inter-service communication, ensuring quick data transfer between microservices.
Continuous Integration/Continuous Deployment (CI/CD) Pipelines:
To streamline development, testing, and deployment processes.
Development Approach:
01
Data Model Analysis:
Rigorous examination of the existing data model to identify optimization opportunities and ensure the new backend would be capable of handling complex analytical queries.
02
Microservice Architecture:
Designing lightweight, independent services, each responsible for specific business logic, facilitating easier maintenance and quicker feature updates.
03
API Layer:
Crafting Go-based RESTful APIs and employing gRPC where high-performance inter-service communication was crucial.
04
Package Development:
Integrating existing Go packages with newly developed ones to extend functionality without reinventing the wheel.
05
Replicated Database Environment:
Setting up a replicated environment to enable real-time data analytics without impacting the main database’s performance.
Outcomes:
Scalability and Performance:
The Go-based microservices significantly improved the application’s ability to handle large-scale operations and complex data processes.
Faster Feature Delivery:
The new backend architecture allowed the client’s development team to add features more quickly and efficiently.
High System Reliability:
The use of containerization and orchestration tools led to higher system reliability and ease of deployment.
Positive Impact on In-House Development:
The client’s team reported a reduction in production issues, allowing them to focus on creating new features rather than fixing old ones.
