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Revolutionizing Retail Business with Generative AI-Powered In-App Analytics
Business Problem
About Taascom
TAASCOM helps companies transform their business model to a subscription-based service model. They achieve this by providing the technology, experience and expertise to enable this transformation.
Solution
To address the identified challenges, Taascom partnered with GoML to implement Generative AI-powered in-app analytics. Leveraging GoML’s expertise and cutting-edge technologies like GPT-4 and Lyzr SDKs, Taascom aims to revolutionize its analytics offerings. The proposed solution involves building an NLP engine atop the existing AWS data stack to deliver proactive recommendations and real-time insights seamlessly integrated into Taascom’ s analytics platform.
Canvas Page Development
The implementation will commence with the development of a Canvas Page within Taascom’s platform. This page will serve as the interface for users to access proactive recommendations and query history seamlessly.
Proactive Recommendations
The heart of the solution lies in providing actionable insights to users based on the latest data trends. Leveraging Generative AI models like GPT-4, the system will analyze real-time data streams and generate proactive recommendations tailored to each user’s needs and preferences.
Real-time NLP-powered Data Analytics
The integration of NLP capabilities into Taascom’s analytics platform will enable users to derive insights from unstructured data in real-time. By leveraging Lyzr SDKs and advanced NLP algorithms, the system will extract meaningful insights from textual data, empowering users with comprehensive analytics capabilities.
API Development
To ensure seamless integration with Taascom’s existing infrastructure, GoML will develop APIs using Lyzr SDKs. These APIs will facilitate communication between the NLP engine and Taascom’s analytics platform, enabling smooth data exchange and interaction.
Amazon Redshift Integration
The implementation will leverage Amazon Redshift for robust data storage and processing capabilities. By integrating with Amazon Redshift, Taascom’s analytics platform will benefit from scalable and efficient data management, enhancing overall performance and reliability.
Architecture
Frontend Interaction: Users access the application through a frontend interface managed by Amazon Route 53 for reliable DNS routing.
Load Balancing: An Elastic Load Balancer distributes incoming traffic across multiple Amazon EC2 instances to ensure high availability and reliability.
Backend Services: The backend, hosted on EC2 instances with Auto Scaling, processes user requests and manages microservices using Amazon ECS (Elastic Container Service).
Data Processing and Storage: Data is stored and managed using Amazon S3 for object storage, Amazon RDS for relational databases, and Amazon DynamoDB for NoSQL storage, with AWS Data Pipeline orchestrating data workflows.
Serverless Functions: AWS Lambda executes specific tasks in a serverless environment, enhancing scalability and efficiency without manual server management.
Security and Monitoring: AWS CloudWatch provides monitoring and observability, while AWS IAM ensures secure user access and permissions across services.
Search and AI/ML Integration: Amazon Elasticsearch Service enables real-time search and analytics, and Amazon SageMaker facilitates the development and deployment of machine learning models.