The Role Of Vector Databases Inside The ‘AI Factory’

AI is hungry. Today’s application of Artificial Intelligence (AI) in modern apps means they are hungry for data. As more enterprise organizations embrace an AI-first approach in the pursuit of efficiency and a new competitive edge, building new generative AI functions and bolstering the use of predictive and causal AI models will be the foundation of what could be called an ‘AI factory,’ which may just industrialize enterprise decision making.

Enterprises are using AI to filter through repositories of their existing data to uncover patterns, provide context and to automate processes and logic to improve the productivity of knowledge workers and customer satisfaction. However, there is something of a chasm between simply having access to 80 billion documents of raw data and feeding it into a black box, compared to strategically using AI to stay ahead of market trends and customer needs.

To achieve the performance necessary to industrialize AI-based decision-making, companies should first think about laying down the requisite data services infrastructure. Much of this is based on capacity for data ingestion, data processing cadence, analytics and search competence across Large Language Models (LLMs). Where traditional databases are argued to have limited computational ability for temporal and unstructured data (spoiler alert: that’s what makes up most datasets), discussion around vector databases has come to the fore in recent months.

As a company that puts its ability to offer high-performance time-series vector data management as its key differentiator, KX talks about how it works with some of the most information-intensive applications that exist across investment banking, life & health sciences, semiconductor development, telecommunications and manufacturing.

Building an AI factory

"As we embrace the future, it's imperative for AI-first companies to transcend traditional task automation and steer towards a holistic enhancement of job processes. This will require advanced technologies," said Ashok Reddy, CEO of KX. "Our ambition (and what we are able to provide customers) is not merely the ability to augment individual tasks but to revolutionize entire job functions. This strategy empowers professionals to pursue roles of greater strategic value and changes how we think about the dynamics between our human workforce and applications of technology."

In Reddy’s view, the essence of bringing the AI factory to life for AI-first companies involves harnessing sophisticated pattern recognition technologies.

“This pattern recognition task requires an array of AI tools, including LLMs and Retrieval Augmented Generation (RAG), which can be used to operationalize and industrialize decision-making processes across organizations. An integrated approach eradicates siloed operations, ensuring synergy across departments, pivotal for the AI-first business model. But it’s important to remember the collaborative role that AI will now play i.e. in the context of AI-first, our goal is to complement, not replace, human intelligence. While AI demonstrates unparalleled efficiency in pattern detection and data processing, it is the human capacity for intuition and contextual insight that guides its application."

When did my data happen?

If this concept of the AI factory is valid, then we’ll clearly need tools and equipment to populate the shop floor. This is the point where we can suggest that AI can enrich a user’s understanding of how time affects their business and provide a level of analysis for informed decision making. Uncovering this level of knowledge from data, including text, video, audio and images is hoped to enhance the ability for AI-first companies to refine and target data searches for better relevancy.

Reddy and team suggest that our newly empowered workers can use this knowledge to build long-term business strategies associated with time-sensitive elements – stock market prices, customer purchasing behaviors etc. Consider the ability for an analyst to examine grain prices above and beyond simply the market value i.e. they could also start incorporating and integrating temporal analysis as it relates to the weather, an election, export bans or a global conflict.

"By applying time-oriented vector-based analytics, we significantly elevate the intelligence layer within AI-first companies, enabling a broad spectrum of professionals, from healthcare to legal sectors, to expand their capabilities,” explains Reddy. “This approach is foundational as we advance AI and LLMs, emphasizing the need for systems-level thinking to innovate workflows and workloads within the AI factory model."

AI-first in practice

Examples of AI-first companies are seen in the financial services industry and government, but also across gaming, e-commerce, manufacturing & energy, plus also aerospace and defense. Then there’s healthcare, which as organizations embrace AI-first will be life-changing.

“The healthcare and life sciences field generates more unstructured data (which really is where the future of AI lies) than any other business,” underlined, Dennis McLaughlin, senior VP of product management at KX. “If we think about an obstetrician’s monitoring console in a maternity theatre (you know, as parodied by Monty Python the machine that goes ping sketch), modern units today are capable of capturing hundreds or thousands of readings per minute. But if our healthcare system only stipulates that a nurse or operative takes readings once every 10 minutes (or half an hour even), we’re missing data that could feed the wellbeing of the AI factory and all its human workers – and that could provide insight that leads to new advancements in patient care.”

“For AI-first companies, the AI factory isn’t just a concept - it’s a strategic framework that shapes the future of business,” concludes KX CEO Reddy. “By prioritizing temporal analysis and systems-level integration of AI, we’re not just preparing for the next phase of innovation; we’re actively constructing it, setting a new benchmark for efficiency, collaboration and strategic insight in the digital age.”

If processors, parallelism and fuzzy or contextual search across time-oriented database structures with a direct optimization for RAG in the AI LLM space spanning both structured and unstructured data were not top of mind for most chief AI officers (CAIO) today, then perhaps they will be tomorrow. The AI factory may soon be kitted out with a new cadre of AI-empowered workers and a corresponding set of smart machines, many of which will go ping at some point - the difference now is we will know ‘when’ the ping pinged.