Enterprise data platforms require analytical based insights for efficient decision making. It mandates data engineers to write complex SQL queries or AI to perform search using trained models which requires time, effort and skills. Additionally, there could be performance issues requiring optimal query constructs, indexing and efficient data handling. Thus making the overall process slow,complex and less user friendly. To enable faster search, vector embeddings are coming into play. Evolving hyperscalar AI frameworks enabled with AI based semantic search taking Natural Language Query (NLQ) as input are easing data engineer’s challenge.
Intelligent Search powered by GenAI, provides a cost effective option for smart search. It enables faster enterprise data search by leveraging open-source software with vector extension (facilitating vector embeddings and indexing) or respective hyperscalar services based on user configuration.
It is powered with dual combination of semantic and keyword based search for complex user queries. User can query in natural language, and the system intelligently understands the context and perform semantic search and retrieve results without any need for writing complex SQL queries manually.
This GenAI-powered search tool, fastens and eases complex search operations and can be extended to any hyper scalar platform. It can be used as an efficient search engine for enterprise applications for meaningful strategic insights aiding in efficient planning and decision-making enhancing user experience.
