Case Study - The easiest way to find influencers

Overview

Social Grab is revolutionizing the way people discover creators on social media platforms through a semantically aware search engine.

We achieved this for Social Grab by using the cutting-edge technology of LLM Generative AI embeddings, and vector databases. The use of vector indexing allows for fast similarity searches, making the engine extraordinarily efficient and context-aware.

The use of vector embeddings in Social Grab's creator search engine stands out as the most effective approach for several reasons. Unlike traditional keyword-based algorithms that are limited to exact matches or closely related terms, vector embeddings capture the nuanced, multi-dimensional nature of a creator's content. This enables the engine to understand the semantic relationships between different pieces of data, effectively allowing it to 'read between the lines.'

For example, a keyword search for "fitness" might only yield creators who explicitly use that term, overlooking those whose focus might be on 'wellness,' 'exercise,' or 'active lifestyles.' In contrast, a vector-embedded search comprehends the underlying thematic unity among these terms. This leads to more comprehensive and contextually relevant results, which are vital for users aiming to discover creators that truly resonate with their interests. This semantic depth, combined with the speed and efficiency of similarity searches in vector spaces, makes the vector embedding approach the gold standard for creator search engines.

The technology isn't just a breakthrough for discovering creators; it has a versatile design that can be adapted for use in various sectors. Thanks to its lightweight, serverless architecture, the platform boasts impressive scalability and speed, making deployment quick and efficient. Given its proven success and adaptability, our company is well-equipped to custom-build this solution for other businesses, making it a wise investment for anyone looking to incorporate AI.

What we did