We started to use the solution for POCs in our organization. So, we are not in a state where we use its services at a full-fledged level. Additionally, we use it to solve our queries and understand whether it is actually helping us solve our problems. Basically, this is how we have been using the solution for a year and a half. Since we are getting results from the solution, we are moving ahead with its implementation in our organization.
We use this solution to understand the intent of our customers when they ask for products. As a result, we can understand user sentiments and predict what they are trying to achieve.
Research Director, Network Security at a tech services company with 10,001+ employees
Real User
Jan 31, 2021
Most of the use cases have been around taking in a lot of human-created data by providers. For example, electronic medical records including the notes, the information, and the clinical aspect of care. What they're typically trying to do is adjust a lot of that type of information. Sometimes there is member feedback or member sentiment that's captured. The idea is to try to quickly assess and analyze that information and then prioritize it. The goal is to put the data in buckets for particular use around quality improvement and around identifying risks earlier, and things of that nature.
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We use different artificial intelligence models to build questions and get answers for clients.
I'm mainly checking how easy it is to use this platform to implement products. My idea isn't very flexible, and it's quite easy to do something basic.
We started to use the solution for POCs in our organization. So, we are not in a state where we use its services at a full-fledged level. Additionally, we use it to solve our queries and understand whether it is actually helping us solve our problems. Basically, this is how we have been using the solution for a year and a half. Since we are getting results from the solution, we are moving ahead with its implementation in our organization.
We use this solution to understand the intent of our customers when they ask for products. As a result, we can understand user sentiments and predict what they are trying to achieve.
We are using this product to do some R&D work.
Most of the use cases have been around taking in a lot of human-created data by providers. For example, electronic medical records including the notes, the information, and the clinical aspect of care. What they're typically trying to do is adjust a lot of that type of information. Sometimes there is member feedback or member sentiment that's captured. The idea is to try to quickly assess and analyze that information and then prioritize it. The goal is to put the data in buckets for particular use around quality improvement and around identifying risks earlier, and things of that nature.
We primarily use the solution for data science purposes. We use it for the deep learning, machine learning, and classification of the data.