In today's fast-paced world, customer service has become a key differentiator for businesses. Insurance companies are no exception, and they strive to provide timely and accurate responses to their customers' queries. However, with the increasing volume of customer inquiries, it can be challenging to maintain productivity levels while delivering exceptional service. This is where Paradigm comes in, with its advanced language model capabilities, It can optimize the productivity of customer service in the insurance industry.
Experience a new world
With the help of AI, an insured or advisor can ask a question in natural language
and get a response in just a few seconds.
User:
"Is my car insured against fire?"
Prompting:
Transmitting user input and contextual informations (eg: user ID) to generate the prompt
Context:
Transmitting contextual informations (eg: user ID, contract ID)
Searching
A search engine performs a search for the most relevant content. (for ex: the user contract)
Embedding
The embedding process is applied to the content selected by the search engine in order to compare it with the prompt.
Chatbot:
"Yes, your car is insured in case of theft under your car insurance policy. "
Source contract page 5, 6 and 11
Improved autonomy
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.