Welcome to Super search, the revolutionary product that can transform the way employees search for information. With Paradigm, employees no longer have to launch multiple apps and sift through numerous files to find the information they need. Our LLM technology allows employees to search from any source with just one search box and retrieve relevant information in seconds.
But what sets Paradigm apart from traditional search tools is the ability to justify answers with different extracts. With this feature, employees not only get the answer to their search query but also understand why that answer is relevant.
Say goodbye to the frustration and inefficiency of traditional search tools. With Paradigm, employees can now find the answers they need quickly and effortlessly. Join the revolution and experience the future of search with Lighton's Paradigm.
Experience a new world
Thanks to our AI technology, employees can now find the information they need in just seconds, all in one place
User:
"What is the momentum project?"
Prompting:
Transmitting user input to generate the prompt
Searching
The user input is shared to all Apps related such as Slack, Drive, Gmail, Click up.... Thanks to their APIs we are able to identify the most relevant content related to the user question
Embedding
The embedding process is applied to the content selected by the search engine in order to generate a text based on information found on all applications.
Search engine:
"The Momentum Project is a research initiative at LightOn dedicated to advancing the state of the art in natural language processing (NLP) and understanding the fundamentals of language."
Source Slack: conversation ID, Drive: document name, Click up: task list name, Gmail: Email content
Improved
accuracy
Increased user satisfaction
Time-saving
Cost savings
2 sec
Average response time to user inquiries.
78%
Rate of inquiries resolved without human intervention.
90%
Customer satisfaction ratings.
-80%
Time savings achieved through automation.
Prompting involves providing a hint or suggestion to the language model to generate more accurate and relevant responses.
Fine-tuning refers to the process of training a pre-trained language model on a specific dataset to improve its performance on a specific task.
Embedding is a process of representing text data in numerical format that a machine learning model can understand and process.
LightOn adheres to strict data privacy regulations and ensures that customer data is stored and processed securely in a private cloud or on-premises environment.
How LLM can improve any search experience?