Social Metrix

Designed With 😇 :

• Netlify
• Nodejs
• Py
• React
• Tailwind

Social Metrix aka (Social Metrics) is an AI-driven analytics tool designed to revolutionize social media engagement analysis. By leveraging Langflow, AstraDB, and cutting-edge AI technologies, this tool provides:

• Real-Time Chat with AI Assistant: Interact with a conversational AI to get detailed engagement insights for social media posts.
• Dynamic Data Visualizations: Visual graphs and charts showcase metrics like likes, comments, shares, and overall engagement across different post types.
• Accurate Engagement Analysis: Advanced computations deliver precise metrics for effective strategy formulation.

Social Metrics is perfect for digital marketers, influencers, and organizations to understand and optimize their content strategies seamlessly.

GitHub Link 🔗

• https://github.com/HackNetAyush/Social-Metrics

Deploy Link 🔗

• https://socialmetrix.netlify.app

Problem it solves 🙅‍♂️

• Analyzing social media engagement can be challenging, with large datasets requiring accurate insights for effective decision-making. Social Metrics addresses this issue by: 1. Automating the data analysis process using AI. 2. Visualizing complex metrics in an intuitive format. 3. Providing real-time, actionable insights to improve engagement strategies across platforms. This tool eliminates manual effort and brings precision to the analysis, making it a must-have for data-driven social media campaigns.

Challenges I ran into 🙅‍♂️

• A significant challenge was dealing with the vast amounts of data stored in AstraDB, a vector database. Connecting AI directly to the AstraDB component for analysis caused the following issues: 1. Inconsistent Results: The AI struggled with large datasets, often performing incorrect calculations. 2. Accuracy Issues: Results for the same query varied significantly, which is unacceptable in data analysis. To solve this: I created a custom Python component to fetch structured JSON data from the database and manually perform calculations. Only the pre-processed, accurate data was then sent to the AI for final analysis. This approach ensured consistency and precise results while fully leveraging the AI's capabilities.