However, all AI isn’t the same. WhizAI’s AI-powered analytics platform is designed for life sciences companies, using a combination of technologies to produce accurate, contextual insights. Furthermore, the platform delivers insights that employees throughout the organization can easily access so that they can use them in their day-to-day workflows.
WhizAI Solves Life Science Companies’ Data Analytics Challenges
- Insights for Augmented Consumers:
The WhizAI platform includes a hybrid natural language processing (NLP) engine that allows users to ask questions in natural language and receive answers that they can easily understand. WhizAI enables conversational experiences for people without “data scientists” in their titles. They don’t have to learn how to use dashboard solutions. All they need to do is ask questions naturally.
- The Ability to Drill Down:
The answer to a query may inspire users to ask additional questions. If a sales team asks to see a history of sales performance, the next question may be how competitors are doing or where a brand is losing ground. With dashboard solutions, it could take weeks to ask follow-up questions or dig deeper to find a root cause. WhizAI enables users to have a conversation with its platform, gain insights into its market, and even determine steps to take to address issues.
- Speed from Data Acquisition to Insights:
WhizAI analyzes billions of data points and delivers insights, automatically choosing the optimal way to visualize data, in less than a second. It automatically updates when new data is available and includes that information in its analyses. With AI-powered analytics from WhizAI, waiting for insights while the data team builds a dashboard or runs reports is a thing of the past.
- Keeping Up with the Data Explosion:
New data and new data sources are continually available. WhizAI allows life sciences companies to easily scale to analyze expanded numbers of data sets and volumes of data.
AI-Powered Analytics Use Cases
All life science company teams can benefit from WhizAI:
- Field Sales: WhizAI enables sales reps to pinpoint viable opportunities, and delivers insights at brand and region level.
- Market Access: Instant access to data insights helps teams stay on top of payer and plan performance and competitor activity.
- Patient Services: WhizAI empowers patient services teams to analyze patient data to identify risks for nonadherence and support treatments that lead to better outcomes.
- Data Teams: WhizAI decreases dependence on IT resources by as much as 60%, giving them more time to spend on complex analyses. WhizAI also gives data teams an easy way to query datasets to determine the most relevant to use in analyses.
- Clinical Development: WhizAI can help decrease the time and costs of clinical trials, give all stakeholders on-demand access to insights, and immediately identify adverse events, enhancing trial quality.
What Differentiates WhizAI in the Marketplace
WhizAI is pretrained on life sciences data, and it’s designed specifically to analyze data to answer complex questions that teams in the industry have. An added benefit of pretraining WhizAI with life science data is that it deploys much quicker than a generic analytics platform built to meet the needs of a horizontal market.
The platform leverages microservices architecture managed with Kubernetes to give its platform limitless scalability and reliability. WhizAI users can add data for analyses easily instead of purchasing additional dashboards and piecemealing analyses to try to find the answers life science teams need.
WhizAI addresses the needs of global enterprises with integrations with business applications, multi-language capabilities, enterprise-grade security, and access control that enables setting permissions based on role or region.
Users can access insights with this AI-powered analytics platform from within the applications they commonly use, such as Veeva or Salesforce, or using their PCs, tablets, or smartphones.
WhizAI will provide relevant, contextual insights out of the box, but as users ask questions and interact with the platform, it continues to learn. It adapts to user behaviors and preferences and tailors future responses based on them.
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