How DataStax is making accuracy generative AI’s greatest asset

As a hybrid vector database leader, DataStax’s roots are in Apache Cassandra®—the open-source database management system running more unstructured data than any other software. Kapoor explains that becoming an AI company was a natural evolution, “There is no AI without unstructured data at scale.” To get there, DataStax built its product on top of the database after vectorizing it so that large language models (LLMs) could understand the data.

Historically as new technologies have emerged, the industry strives for three qualities: scalability, reliability, and availability. When it comes to generative AI, scalability and availability are muscles that are already being flexed, and reliability typically grows over time. But Kapoor asserts that there’s one new muscle that still needs to be trained—accuracy.

“People will start paying more for accuracy just like they paid higher for 99.99 percent availability,” he predicts. DataStax believes that we’re just beginning to see what’s possible with AI; with near real-time results and greater accuracy, it will be truly transformative.
 

While LLMs provide the content for generative AI apps, smart context is essential to creating the most relevant outputs and reducing hallucinations. As Kapoor notes, “You cannot create accuracy based on LLMs alone.” To get there, DataStax uses techniques like retrieval-augmented generation (RAG) to bring content and context together iteratively.

The results of their innovations mean that developers can unlock complex, context-sensitive searches across diverse data formats, gaining 20 percent higher relevancy and 75 times faster query responses for generative AI applications. Meanwhile, DataStax’s product Langflow simplifies RAG, making it easy for developers to experiment and deploy apps. Whether they’re building agents or personalized customer experiences, they have the tools to create far better outputs.

In enabling more experimentation, DataStax expects to see a dramatic gearshift for generative AI. Kapoor notes, “There is no direct path to innovation. You cannot write the recipe until you have tried it and failed a few times, right? By documenting what’s working and focusing on the product market fit, you can start taking projects into production as quickly as possible.”

Having managed large enterprises’ most mission-critical workloads, DataStax built on a solid foundation of trust when transitioning from a data company to an AI partner. It has now supported hundreds of proof-of-concepts (POCs) to transform customers’ business models. The key ingredients for making the path to production easier, faster, and more accurate have been Amazon Web Services.

For instance, DataStax integrated Amazon Bedrock for easy, scalable access to foundation models (FMs) and Amazon SageMaker to unify all their data. AWS Glue also empowers the business to drive accuracy by preparing and integrating disparate data, while AWS PrivateLink simplifies the task of securing their customers’ sensitive data.

What’s more, hands-on support from AWS engineers and a customer obsession mindset have helped DataStax break new ground. “I love that AWS is always hungry to do more and open to doing things differently,” Kapoor says. “Because we’re on the cutting edge of technology, we can build something before the market knows it needs it,” he continues. “That’s where the magic happens.”

Looking ahead, the business anticipates multi-agent platforms to become capable of work completion, not just work enablement. Kapoor envisions agents being able to talk to one another and deliver contextually appropriate responses and actions. “Agents will be the only way we build software in the future. Right now, we’re collaborating with AWS to make it happen,” says Kapoor.

By building products that developers love, DataStax enables them to influence the growth trajectory of enterprises. With a strong focus on people, processes, and technology, it helps customers shape increasingly meaningful and accurate outputs. As positive developments continue, the company believes AI will become a true force for good in society. Learn more about what AWS can do to help software and technology companies thrive in the realm of AI.