On KX Core Technology. Q&A with Michael Gilfix

 Pretty soon, we’re going to expect our AI systems to have real time awareness and be knowledgeable about what happened a millisecond ago, not a month or a year ago.”

You have been recently appointed Chief Product and Engineering Officer at KX. You have more than 20 years of experience in software, along with a wealth of expertise in the areas of data, AI, integration, and automation. What are the main lessons you have learned in all these years? 

That’s a big question to kick off with! I’ve worked on a wide variety of technologies throughout my career. What I’ve seen first-hand is the transformative power that being data centric can have on a business. It can transform the customer experience, drive greater operational efficiency, and make better commercial decisions. And it’s becoming easier, and faster, to do all of this, and more at real time speed. This was why I was really excited to join KX. 

Our technology is at the forefront of two things that unlock the business value of data: improving real time decision making and enabling a next wave of productivity through use of generative AI.

The second thing I’ve learned is that if you have a great product team, point them at the right customer problems, and empower them to be creative about how to solve those problems, then amazing things can happen.

What’s special about the team at KX and your core technology?

I don’t think I’ve ever met a team that is more passionate about solving customer problems and creating value than the team at KX. The expertise that exists for building data systems that can process large amounts of data with unparalleled performance and incredible efficiency is exceptional. And that’s our secret sauce. Our technology is built on an engine that can process real-time, time series, and vector data in a way that no one else can; it unlocks a myriad of different use cases at a scale that no one else can implement.

I’m both extremely proud and excited to lead a team that is delivering technology that has such a profound impact on the enterprise market.

Generative AI has bought enterprises the promise of a next level productivity tool that can be as revolutionary as the creation of spreadsheet software. But generative AI engines need a data layer to ensure they can keep public and private enterprise data up to date, because users will expect their AI to give them answers to their questions with real time relevancy as well as accuracy. 

For example, business users asking ChatGPT about product portfolio performance would expect it to answer with recent performance data and over appropriate comparable time periods. KX offers technology with the scale to keep that knowledge base up to date in real time, so it can provide contextual answers to the AI delivering far more accurate, and relevant results.

Enterprises are also constantly looking to improve the speed and quality of decision making and thus have put a big focus on real time analytics. Better decision making means you can better serve your customers’ needs, make better product recommendations, implement more effective pricing, and determine when corrective action is needed. KX brings technology that can process and crunch this data at tremendous scale so it can keep pace with the data demands of a modern enterprise.

A great team, cool problems, and great technology is why I joined KX.

What are the core components of KX technology and how do they all fit together? 

At the heart of all our products is the KDB+ engine that can do incredible parallelized processing, high performance vector processing, understand sequences of data over time and ingest massive amounts of data in real time. The system works with real time and historical data in a unified way. 

KX offers three products designed to serve a variety of enterprise needs:

  1. KDB+: An embeddable engine that can supercharge applications with unmatched performance for time series, tick data, and vectorized processing
  2. Insights: Real time analytics designed to improve the speed and effectiveness of business decision making at scale. We give customers stream processing, data pipelining, real time visualization, and embedded machine learning for forecasting.
  3. KDB.AI: A vector database that can take public and private enterprise data and enable large language models and AI generators to search and reason about that data. This data can be kept up to date in real time and our vector database can use time as context to help AI engines answer business questions more effectively.

All of these products are built on a shared architecture so our customers can use integrate these capabilities seamlessly in their end-to-end enterprise applications.

What does it mean in practice to have a “Time-sensitive AI”?

Pretty soon, we’re going to expect our AI systems to have real time awareness and be knowledgeable about what happened a millisecond ago, not a month or a year ago. One of the challenges with traditional AI technologies and indeed with large language models (LLM) is that they are trained on old data. They have no awareness or understanding of what’s happening now. 

A key strength of KDB.AI is that it allows developers and data scientists to bring real time awareness and relevancy to their AI-powered applications and models, making them smarter. We enhance a company’s ability to spot anomalies and predict occurrences with greater speed and accuracy. 

Who are your typical customers?  

Our customers are immersed in highly data-driven sectors, from financial services to life sciences, healthcare, energy and utilities, manufacturing, government, and defence. Our vector database can be used by any enterprise that is leveraging natural language processing and generative AI. It can serve use cases big and small, and customers can be assured that an investment in our technology will keep pace with the demands of their growing data needs.

We also work with systems integrators and other technology partners that embed KX technology withing their products to industry-specific solutions, such as McLaren Applied for motorports and automotive, Fingrid for energy providers and of course, our many partners in the financial services sector.

Why should customers choose KX?

Firstly, KX is a proven technology. It was birthed in some of the most demanding and mission critical financial services use cases and has since been scaled to other industries. 

Secondly, KX offers unparalleled scale, price performance, and efficiency – including energy efficiency that delivers incredibly attractive economics to enterprises.

Do you have any resources you can share on Vector databases?

We’ve put a great deal of effort into making sure our products are incredibly easy to use. We want to democratize access to our technology so developers and data scientists can quickly and easily use our products to build production-ready applications and see how game changing they can be.

Our evangelism team has created a comprehensive range of materials to show how easy it is to use our technology and demonstrate practical examples. You can find them all here: [link]

As a leading player in the global generative AI sector, we also understand that our customers will want to have a broader understanding of the technology, the opportunities and challenges that are presenting themselves in the space and learn from experts in the field. To that end we support a Substack called Vector Database Central. It gives a balanced, informative overview on what’s happening in the world of vector databases and generative AI, with articles written by experts in the field. I’d encourage your readers to subscribe (it’s free), join the discussion and of course, give us feedback on topics they’d like to see covered.

Is it possible to register an interest to try out KDB.AI?

We recently announced the launch KDB.AI Cloud, a SaaS based free to use version of KDB.AI. Quick and easy to set up and use, KDB.AI Cloud enables developers to bring temporal and semantic context to their AI-powered applications, giving business users the ability to ask the right questions using natural language search. 

It’s unique among vector database offerings in that it can handle high-speed, time-series data and multi-mode query data processing. This allows it, for example, to query real-time financial market data using natural language with semantic relevance. Its inherent temporal awareness means it can answer questions based on ad-hoc time windows such as data creation, modification recency, or periodic comparisons. This helps applications find and return more relevant data and allows for point-in-time and like-for-like comparisons.

Your readers can go to the website and in just a few minutes start building production-ready applications. We’re really excited about this product as it democratizes access to our technology and gives all companies the ability to see just how transformative our technology can be to their business.

What are your plans ahead?

Most firms are focused on the basic AI use cases of taking their data, indexing it, making it accessible to generative AI engines. But this doesn’t solve a lot of the problems for how people want to use the technology. For example, people want ChatGPT to know when something occurred, be able to compare different points in time, and do like for like compares (e.g., compare 1st half of this year to 1st half of last year). This is something people innately do and requires knowledge of time. Our engine has this time capability built in and therefore is better positioned to answer these questions without sacrificing performance. 

Going back to my answer on what makes our team and technology so special, we have the experts who want to solve these problems for the benefit of both customers and society in general.

Can you share some of the customer implementations that you’re most excited about?

Our work with Syneos Health to improve clinical trial efficiency and shorten time to market for life-changing therapies for patients is ground-breaking. We’re empowering biopharmaceutical customers to better solve complex healthcare decisions through data analytics and AI. [link]

In the financial services sector, the work we’re doing with brokerage firm Virtu Financial to democratize access to KDB+ is really exciting. A report that used to take eight hours to complete in SQL is now being done in 5 minutes with Python and KDB+. That shows the deep interoperability between Python and KDB. [link]

We also have some really exciting and innovative use cases currently being developed using KDB.AI across a range of industries and I’m looking forward to sharing more on those as they get released.

Finally, I’d like to highlight our work with global system integrators and other data and technology partners to further the reach of KX technology. We’ve signed a host of major partnership announcements where our technology is being integrated into solutions that promise transformative results across a range of industries. You can read more about them here: [link


Michael Gilfix, Chief Product & Engineering Officer, KX

Michael is an experienced software business executive with a strong track record of driving growth and building scalable global software product businesses for the enterprise market. At KX, his focus is on driving a product-led software strategy, accelerating market growth by democratizing access to KX’s market-leading technology, principally the KDB.AI vector database.

With over 20 years of experience in software development Michael held a number senior positions at IBM, leading teams in product management and engineering for data, AI, integration, and automation technologies. Most recently, he served as an advisor to the leadership team at Domo Inc, a cloud business intelligence company, on product and business strategy. 



Sponsored by KX.

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