Life Science Intelligence that Keeps Up with a Changing Industry

WhizAI enables companies to overcome the hurdles they face with business intelligence (BI) dashboard solutions and gives employees throughout the organization easy access to the insights that help them enhance their performance.

The WhizAI Life Science Intelligence Solution

WhizAI solves life science intelligence business problems, including:

Time

Building dashboards, analyzing data, and digging deeper for additional insights can take weeks or months from the time a company acquires data until employees can get the actionable answers they need.

WhizAI analyzes billions of data points from multiple data sources in less than a second to enhance workflows and processes, not slow them down.

Scalability

Legacy life science intelligence dashboards don’t scale very well, resulting in teams using multiple screens to try to piecemeal analyses for the insights they need.

WhizAI’s platform easily scales to add new data sources and analyze larger data volumes, removing limitations that data teams face with their legacy systems. Furthermore, they can access those insights from the applications they routinely use, such as Salesforce, Microsoft Teams, or Veeva.

Domain expertise

Although there are several business intelligence solutions on the market, even a variety that leverages artificial intelligence (AI), there is only one choice designed specifically for life sciences by a team with industry expertise: WhizAI.

WhizAI’s platform is pretrained for life sciences data so that it deploys quickly and provides contextual insights out of the box. Additionally, the workflow within WhizAI closely aligns with how life science teams organize and share data insights.

User-friendliness

Many employees throughout life science companies have limited data science expertise, and they must spend hours in training sessions – but may still not be able to use legacy life science intelligence solutions comfortably and proficiently.

WhizAI is designed for the augmented analytics consumer. Natural language query (NLQ) allows users to ask questions conversationally, and the platform understands them and responds with contextual answers in a near instant.  WhizAI’s hybrid natural language processing (NLP) engine also understands users’ intent, even if they misspell words or phrase a question in an unusual way. Additionally, WhizAI continues to learn with us, tailoring responses to user preferences and behaviors.

Case Study: Uncovering Root Causes with WhizAI

One life science company wanted to get to the root cause of why sales of a particular brand were slowing. A WhizAI user asked the question in natural language: “Why are our sales down?” The platform understood that the user was referring to TRx. Then, using patient data claims data, and other data sources, the platform confirmed that sales were slowing by presenting historical data over time.

Next, the user asked the platform to show the 13x13 change for claim counts for the brand and its key competitor. The user only had to ask – no coding or phrasing queries in a certain way were required.

The platform showed that rejected claims for the brand had increased by 17% over the specified period, but that metric was steady for the competitor. Additionally, the platform revealed that the rejected claims originated at the intermediary level, providing the company with direction to correct the issue.

Also note, WhizAI was able to provide these insights in just minutes rather than the weeks it would have taken to run analyses and build new dashboards to uncover the root cause.

What Companies Can Achieve with a WhizAI Life Science Intelligence Solution

WhizAI enables companies to achieve their goals for life science intelligence, including:

• Dynamic, Omnichannel Engagement
  WhizAI allows users to access data insights and share them at any time and from within the systems and applications they typically use.

• Opportunity-Driven Sale
  With instant life science intelligence, commercial teams can pinpoint top opportunities and target their efforts with personalized, targeted marketing.

• Improved Data Analytics TCO
  WhizAI decreases dependence on life science data and IT teams, lowering data analytics total cost of ownership (TCO) by 60% or more.

• Stakeholder Engagement
  WhizAI makes data insights available to everyone within a life science organization, enabling all teams to use current data in analyses and supporting greater team alignment.

• User Adoption
  A life science intelligence platform can only provide value if a company’s teams use it. Companies that deploy WhizAI see adoption as high as 100% due to the platform’s ease of use, accurate insights, and responsiveness.

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Frequently Asked Questions

If we have invested in BI dashboards and other AI solutions, do we have to replace them with WhizAI?

No, WhizAI co-exists with other platforms. Dashboards have a very important role in scenarios like QBR or management performance reviews. WhizAI can work with them to make insights more accessible, especially when users are in the field.

How does WhizAI differ from horizontal AI-powered platforms?

Horizontal platforms provide search-based analytics and are often marketed to a range of industries. In contrast, WhizAI is a life sciences-focused analytics platform pre-trained on life sciences business processes and providing contextual analyses.

Can WhizAI work behind our firewall?

WhizAI has a flexible deployment approach. It can be deployed with your firewall, on-premises, or in a public or private cloud.

Which cloud providers support WhizAI?

WhizAI architecture allows the platform to be deployed on all kinds of public or private clouds, including AWS, Azure, and other popular options.

How difficult is it to implement WhizAI require?

Setting up WhizAI takes minimal time for your team. We typically need assistance from the business’s team to access data and understand client-specific nuances, typically about three hours per week for two weeks. WhizAI also relies on the business’s team during testing, which takes about 6 hours per week for one to two weeks, and two or three 30-minute training sessions for users.