Business Problem

Effective communication and collaboration are critical for success in the modern corporate environment. However, organizations often face challenges such as:

• Diverse Communication Styles: Employees need help communicating effectively due to varying preferences and styles.
• Insufficient Customized Interaction Support: Generic communication tips must address individual needs.
• Weak Team Cohesion: Teams need more understanding of team members to build strong relationships.
• High Turnover Rates: Miscommunication and interpersonal issues not only lead to employee dissatisfaction but also significantly contribute to high turnover rates, posing a serious threat to organizational stability.

Solution

In addressing SurePeople's business problem of enhancing interpersonal interactions, Goml's solution leverages advanced AI-driven techniques to offer tailored meeting tips:

Customized Meeting Tips: The module processes user inputs, analyzing Prism profiles using Amazon EC2 with Lyzr SDKs and RAG Pipeline to deliver personalized meeting tips based on individual communication styles and personality traits.

Continuous Improvement: Feedback is integrated into the system to enhance the accuracy of recommendations, with data management facilitated by MongoDB and workflow orchestration handled by Apache Airflow.

Real-Time Processing: The system utilizes Docker and Data Processing APIs on Amazon EC2 to generate actionable insights in real-time, ensuring timely and relevant advice.

Efficient Data Handling: Amazon S3 stores guides and profiles, while MongoDB Atlas manages vectorized data, ensuring scalable and effective data storage and retrieval.

Architecture

• Use Amazon S3 to store guides, frameworks, Prism profiles, and meeting data.
• Implement MongoDB for data storage, orchestrate data workflows with Apache Airflow, and use Webhooks for real-time data updates.
• Deploy Amazon EC2 instances for running Lyzr SDKs, RAG Pipeline, Docker containers, and Data Processing APIs for transforming and analyzing data.
• Use MongoDB for semi-structured data storage.
• MongoDB Atlas for vectorized data and integrate real-time processing output with the Meeting Module and Generative APIs.
• Manage user queries and metadata through Amazon API Gateway, which refreshes data from MongoDB/Airflow/Webhooks to Amazon S3.
• Ensure seamless data flow from processing APIs to the static data warehouse and real-time processing outputs.