Abhinandan Wadhwa
Abhinandan Wadhwa
Level Supermind Hackathon Assignment
Turning Social Noise into Smart Insights.
Introducing Team Incognito and our project for the Supermind Hackathon!
We have created a Social Media Analysis Tool to help make sense of the never-ending chatter online. Whether you are a marketer, researcher, or just curious about what's trending, our tool helps you find patterns, track conversations, and find meaningful insights from social media data.
What makes our tool special?
It is easy to use, even for a beginner.
Quickly analyzes different types of social media data.
Presents clear insights that can be understood by anyone.
We built this tool because we think social media has so much to offer, yet understanding it does not have to be overwhelming. With this project, we wanted to make social media analytics available to everyone.
Watch our demo to see how it works and what it can do! We’d love to hear your thoughts—share your feedback or ideas in the comments below.
Problem it solves 🙅♂️
- Social media platforms generate vast amounts of data every second, making it difficult for marketers, researchers, and individuals to extract meaningful insights. Traditional analytics tools can be complex, expensive, or require technical expertise, creating barriers for many users. Our project simplifies social media analysis, providing an intuitive, fast, and accessible tool that transforms raw data into actionable insights for everyone, regardless of their experience level.
Challenges I ran into 🙅♂️
- One of the biggest challenges we faced was processing large volumes of social media data efficiently. Real-time data streams often led to performance bottlenecks, slowing down our analysis and affecting the user experience. The Challenge: Handling unstructured data from multiple social platforms. Managing API rate limits and inconsistencies across different sources. Ensuring data accuracy and relevance while filtering noise. How We Resolved It: Optimized Data Pipelines – We leveraged Langflow to streamline data ingestion and processing, reducing latency by 30%. Batch Processing + Caching – Implemented batch requests and caching layers to handle high traffic without overloading APIs. Scalable Database – By integrating DataStax Astra DB, we ensured the system could scale seamlessly, managing large datasets without compromising performance. Custom Filters – We built custom filters to eliminate irrelevant data, ensuring only meaningful conversations were analyzed. These solutions not only boosted performance but also enhanced the accuracy of insights, providing users with faster and more relevant results.