How Relevance AI Uses Advanced Analytics, AI and ML

A lot of people ask us what parts of Relevance AI use advanced analytics, AI, or Machine Learning. This article covers everything about Relevance AI's core capabilities.

How Relevance AI Works

There are 5 main features that make up Relevance AI's core capability:

  • Ingest and Vectorize—Relevance AI's data contextualization algorithm. This is where data is uploaded, analyzed, and contextualized by Relevance AI.
  • Detect Patterns —identifying patterns in unstructured data to help users identify key themes. Determine emerging themes by utilizing several machine learning techniques such as cluster analysis, topic modelling, tagging and PCA to highlight key themes in your data.
  • Enrich and Extract —proactively surfacing insights both hidden and unspecified within the data through a range of different algorithms including sentiment analysis, emotion extraction and noun extraction.
  • Visualize and Explore—Relevance AI's ability to automatically create insights and dashboards based on what's changed in your data, both with or without human input.
  • Semantic search —Relevance AI's ability to find documents, based on similiarity, context and relevance, not keyword matching.

Related Articles - coming soon!

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How is accuracy measured in Relevance AI?


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