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RAG Empowered Enterprise Search to Optimize Relevance in your Search

Generative AI Combined with Enterprise Search Pushes the Boundaries of Possibility


In a world where digital workplaces are constantly evolving – Forrester estimates an average of 367 business applications are used by large enterprises* – information retrieval has never been more crucial to remain competitive in the market. To meet this growing demand for rapid, reliable, and accurate results, generative AI offers new perspectives in knowledge management within companies. However, the information used to generate the response is limited to the data used to train the AI and may not take into account the specific information about the company’s product or service. This is where RAG (Retrieval-Augmented Generation) emerges as a winning technology.

Built on two AI components: retrieval models and generative models, this innovative approach allows for finding information and creating relevant content based on context. The RAG technology significantly enhances the accuracy and relevance of responses by considering specific company information and greatly reduces the risk of AI hallucinations. In other words, a search engine powered by this technology unlocks your data and delivers relevant responses.

By providing quick and accurate responses to queries, RAG-powered search saves time and boosts productivity. It streamlines digital transformation efforts by facilitating the search and use of critical data. By delving deep into data, it also optimizes resources and strengthens decision-making. Search no longer stops at simple keyword searches but becomes a powerful conversational tool.

In other words, this new search model can:

  • Improve knowledge sharing and increase efficiency
  • Enhance customer experience and foster loyalty
  • Uncover hidden insights to make data-driven decisions
  • Accelerate innovation

Gartner predicts that 80% of companies will have generative AI applications in production by 2026.

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*The Crisis of the Fractured Organization, Forrester

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