How Membit Works

Step 1: Data Hunters Contribute Content

The process begins with our global network of Data Hunters. Using a browser extension, they capture relevant social media posts, news articles, and other online content as they browse. This human-in-the-loop approach ensures that we are sourcing content that is genuinely interesting and significant.

Step 2: Verification and Filtering

Once submitted, the raw data enters our distributed infrastructure for validation. Automated checks and AI classifiers work to filter out spam, duplicates, and irrelevant content. This ensures that only high-quality, timely information proceeds to the next stage.

Step 3: Clustering and Analysis

The verified posts are then transformed into vector embeddings. An unsupervised clustering algorithm groups semantically related posts into “discussion clusters,” representing distinct narrative themes. We also apply a time-decayed engagement score to prioritize the most active and relevant conversations.

Step 4: API Delivery

The final, structured context is made available through our developer-friendly API. AI developers can easily integrate this real-time data feed into their applications using our RESTful endpoints or the Model Context Protocol (MCP) server.