RagaAI Collaboration: Building a Robust AI Testing and Monitoring Platform
As a founding member, Origins-AI collaborated with RagaAI to build a robust AI testing and deployment platform from the ground up. The work involved developing comprehensive testing solutions for applications ranging from computer vision to structured data models, facilitating seamless integration and efficient sprint processes. Key achievements included the creation of a drag-and-drop RAG (Retrieval-Augmented Generation) platform, effective team leadership, and introducing Open Telemetry for improved observability.
About Raga
Raga India, a subsidiary of Raga.com, launched in 2013, has become a leading e-commerce platform in India, offering a vast array of products across categories such as electronics, fashion, home essentials, and more. The platform provides services like Raga Prime, which includes benefits such as free and fast delivery, access to Prime Video, and exclusive deals. Raga India also supports local businesses through initiatives like Raga Saheli and Raga Karigar, promoting products from women entrepreneurs and artisans. Additionally, the company has invested significantly in infrastructure, including fulfillment centers and data centers, to enhance customer experience and support its operations in the country.
Our Partnership with Raga
Industry
Artificial Intelligence and Machine Learning
Services
AI Platform Development
Testing Automation
Application Monitoring
Business Type
AI Startup
Technologies Used
Computer Vision
Open Telemetry
Structured Data Models
Automated Testing
The Challenges
Team Coordination & Integration
Ensuring effective collaboration among Engineering, Data Science, and Product teams was challenging due to diverse expertise and differing project goals. This required a structured approach to maintain alignment across teams and achieve cohesive project outcomes.
AI Testing Complexity
Developing a unified testing platform for different AI models, including computer vision and structured data, posed significant complexity. Each model type had unique testing requirements, necessitating a streamlined approach to manage diverse data and output types efficiently.
Monitoring & Observability Accuracy
Establishing real-time monitoring for various application layers, particularly in batch and streaming modes, required a robust tracing mechanism to maintain application stability and traceability in production.
Automated Testing Pipeline
Setting up an automated testing pipeline for pre- and post-deployment was challenging, as it required consistent updates and modifications to support the evolving codebase, ensuring each deployment met quality standards.
The Solutions
Cross-Functional Team Processes
Established regular communication channels and alignment sessions between Engineering, Data Science, and Product teams.
Unified Testing Platform Development
Designed a flexible testing platform capable of accommodating various AI models. Consolidated test cases for different model types, allowing efficient testing without compromising on accuracy.
Enhanced Observability with Open Telemetry
Integrated Open Telemetry to generate traces, providing comprehensive visibility into application performance. This allowed real-time monitoring and quick diagnosis of potential issues.
Automated Testing Implementation
Built an automated testing pipeline that handled pre- and post-deployment scenarios, covering various stages of model validation.