Data That Speaks Your Language, Instantly: Why WhizAI Outperforms Traditional LLMs in Life Sciences Analytics

Rohit Vashisht

Data That Speaks Your Language, Instantly: Why WhizAI Outperforms Traditional LLMs in Life Sciences Analytics

The Generative AI Revolution

November 30, 2022, marked a pivotal moment in the AI landscape. ChatGPT became widely accessible, transforming skepticism into excitement among business leaders. They witnessed firsthand how an AI could genuinely understand conversational queries and sift through massive datasets to deliver accurate, relevant answers. It was a revelation, showcasing the power of generative AI and large language models (LLMs).

However, the pendulum has swung from skepticism to overestimation. While LLMs can perform impressive tasks like composing music or writing code, we are still far from their full potential. Imagine the internet in the 1990s—promising, yet far from today's reality. Similarly, LLMs hold vast potential, but we're only scratching the surface.

The Value Proposition

From BI to GenAI: It's time to move from traditional business intelligence (BI) to generative AI (GenAI). Business leaders and users need more than just dashboards and reports. They require fact-based insights that drive informed decision-making and tangible results. WhizAI delivers actionable insights, enabling users to go beyond static data views and truly move the needle in their operations.

The Build or Buy Dilemma: The hype around GenAI and LLMs has propelled life sciences companies and other businesses to explore these technologies. The pressing question is: Should they build or buy? WhizAI provides the answer.

Embedding LLMs into WhizAI

WhizAI has been a generative AI platform from its inception in 2018, even before LLMs became mainstream. Our proprietary NLP engine combines deep learning with linguistic techniques, allowing users to ask questions conversationally and receive accurate, contextual responses in less than a second.

When LLMs became widely available, WhizAI conducted a comprehensive model comparison to explore how this technology could enhance our solution. The results were transformative. We embedded a domain-tuned LLM into our platform, enhancing our ability to deliver instant, precise insights for life sciences.

Why WhizAI Excels

Data Interaction: Imagine talking to your data like a colleague, essential for making quick decisions in pharma analytics. WhizAI’s proprietary NLP engine and domain-tuned LLM make this a reality. Unlike traditional keyword-based searches and text-to-code translations, WhizAI understands user intent and business context, providing instant visual responses that are both accurate and actionable. As shown in the example below:

*Comparing against competitors brand via simple worded question and instantly generating a visual response.*

Cost Efficiency: Training LLMs require significant time, resources, and expensive GPUs. For life sciences companies, this can mean millions of dollars in investment. WhizAI, pre-trained to answer analytics questions specific to life sciences, offers a quick, cost-effective implementation. Additionally, WhizAI operates as a service, eliminating the burden of running, maintaining, and fine-tuning the solution, resulting in a lower total cost of ownership (TCO).

Data Privacy: Open-source LLMs often raise data privacy concerns, as they can learn from and use data in responses to other users. WhizAI ensures data privacy and compliance by operating in a secure, on-premises environment, accessible only to authorized users and licensed developers.

Minimizing Hallucinations: Traditional LLMs can "hallucinate," providing incorrect answers. In controlled environments, their accuracy can be less than 70%. WhizAI tackles this by providing instructions to the embedded LLM on output structure, suppressing the creativity factor, and implementing post-processing guardrails. The result is a high degree of accuracy, offering users trustworthy information for the best outcomes.

*Unknown custom time expressions can lead to LLM hallucinations. With our fine tuning of LLMs, we are able to ensure that LLM understands these expressions and provides an accurate output.*

User Experience: WhizAI aims to make data analytics insights accessible to life sciences users, regardless of their tech proficiency. Users don’t need to write code or formulate complex queries. They can simply ask questions and receive answers. WhizAI integrates seamlessly with applications like Microsoft Teams and Veeva, making data-driven decision-making a natural part of workflows and enhancing operational efficiency.

WhizAI: A Vision for the Future

WhizAI’s blend of NLP and LLM technology not only addresses current challenges but also positions users for future advancements. One exciting development is data storytelling, which will automatically highlight key information from dashboards. Moreover, WhizAI's improved learning capabilities mean it can understand new datasets and generate accurate insights without additional training.

Embedding LLM into our proven GenAI solution has expanded WhizAI's ability to deliver on-demand analytics insights to life sciences business users. It offers life sciences companies all the advantages of LLM without the associated time, costs, and risks of open-source versions. WhizAI provides a practical, proven way to enhance efficiency, performance, and competitiveness with GenAI today.

Conclusion

WhizAI stands at the forefront of GenAI-powered analytics in life sciences, outperforming traditional LLMs by combining our proprietary NLP engine with domain-tuned LLM, and a user-centric approach. Our platform transforms complex queries into precise, contextual responses, enabling informed, strategic decisions. Experience the future of life sciences analytics with WhizAI. Request a demo or contact us to learn more about how we can help your organization harness the power of GenAI.