Written by
Christopher Aberger
,

Vertically integrated AI is the answer to legacy data problems

Executive Summary:

In today's data-driven landscape, organizations face a significant challenge in realizing the full potential of their data. Despite the recognized importance of data as a paramount organizational asset, a recent executive survey reveals a stark misalignment in perceived hurdles. While only 7.5% attribute challenges to technological deficiencies, an overwhelming 93% identify people and process issues as the primary impediments to successful business adoption. As a result, a whopping 92% of data workers' time is spent on low-value tasks where 84% of them report being dissatisfied with current analytics tools. All told, the promise of self-service analytics remains elusive still today.

The recent surge in AI innovation, driven mainly by large language models (LLMs), has renewed the hope for self-service analytics. Notably, we can now see a future where every data warehouse integrates LLMs as collaborative co-pilots to accelerate analytics teams and remove long standing impediments. But, as it stands, this vision is still in its infancy. Most solutions today tack on AI to their existing data stacks via third party APIs, only to eventually realize that such a shallow approach does not actually address real process issues. This treats AI as yet another tool for data analytics, rather than a lasting organizational asset. To truly address the legacy people and process challenges hindering the realization of self-service analytics, what's required instead is a vertically integrated AI solution that is customized and owned by each organization.

To address these challenges Numbers Station has pioneered a vertically integrated AI architecture. Our cutting-edge architecture puts AI as an intelligent co-pilot directly at the center of an enterprise's data infrastructure, enhancing human productivity and streamlining processes by enabling natural language interaction for data requests.

Our architecture is built upon years of research at Stanford University where we developed state-of-the-art LLMs and a comprehensive architecture tailored for conversational analytics. The result is a vertically integrated approach that guarantees secure outputs, tight human in the loop interaction, and effortless integration into enterprise data systems. This architecture continuously learns and evolves alongside your organization, amassing greater value over time. And, unlike a standalone tool, as it improves with proprietary knowledge enterprises maintain ownership of this invaluable asset.

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