Discover How Squirro's AI & Semantic Search Is Revolutionizing Enterprise Search. Transform Your Data Strategy Now!
Discover How Squirro's AI & Semantic Search Is Revolutionizing Enterprise Search. Transform Your Data Strategy Now!
Retrieval Augmented Generation (RAG) is an AI framework designed to enhance the accuracy of responses generated by LLMs. It achieves this by incorporating external sources of knowledge to complement the model's internal understanding.
SquirroGPT uses RAG technology. It uses Squirro's Semantic Search before it queries the Large Language Model (LLM).
Once the user enters a prompt, SquirroGPT queries the knowledge base - the data and documents ingested.
SquirroGPT then sends relevant information with the query to the LLM. As an extra layer of verification, the response is validated against the knowledge base before passing the answer back to the user.
Chat with all of your organisational data - define the data sources and permission rights that work for your business unit
Each answer is supported by evidence - a reference source
Make your chat available to a wider audience through embedding
Find and interact with data of interest without having to open a single document
Our RAG technology provides more precise results by locating and analyzing relevant paragraphs instead of entire documents -reducing hallucinations
The chat only accesses and analyzes documents the user has access rights to
Experience your documents in a new way and interact with them conversationally
Retrieve all the information you want from a single document
Verify the precision of the answers provided by clicking on the evidence chip and cross referencing with the highlighted text in the document
Four different types of summarization are supported:
Document summary
Topical summary
Page summary
Selection summary
Provide additional instructions to personalize summaries
Combine workplace search with contextually aware chat
Conversationally interact with internal and external data sources to generate personalized insights and boost your productivity
Analyse and visualise structured data in your chat interface
Parse and create conclusions from large amounts of data - simply by asking questions
Retrieval Augmented Generation (RAG) is an AI framework designed to enhance the accuracy of responses generated by LLMs. It achieves this by incorporating external sources of knowledge to complement the model's internal understanding.
SquirroGPT uses RAG technology. It uses Squirro's Semantic Search before it queries the Large Language Model (LLM).
Once the user enters a prompt, SquirroGPT queries the knowledge base - the data and documents ingested.
SquirroGPT then sends relevant information with the query to the LLM. As an extra layer of verification, the response is validated against the knowledge base before passing the answer back to the user.
Chat with all of your organisational data - define the data sources and permission rights that work for your business unit
Each answer is supported by evidence - a reference source
Make your chat available to a wider audience through embedding
Find and interact with data of interest without having to open a single document
Our RAG technology provides more precise results by locating and analyzing relevant paragraphs instead of entire documents -reducing hallucinations
The chat only accesses and analyzes documents the user has access rights to
Experience your documents in a new way and interact with them conversationally
Retrieve all the information you want from a single document
Verify the precision of the answers provided by clicking on the evidence chip and cross referencing with the highlighted text in the document
Four different types of summarization are supported:
Document summary
Topical summary
Page summary
Selection summary
Provide additional instructions to personalize summaries
Combine workplace search with contextually aware chat
Conversationally interact with internal and external data sources to generate personalized insights and boost your productivity
Analyse and visualise structured data in your chat interface
Parse and create conclusions from large amounts of data - simply by asking questions
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