Deep Information Sciences, Inc.

About Deep Information Sciences, Inc.

Deep Information Sciences, Inc., founded in 2010 by Thomas Hazel and Jason Jeffords, operated from Boston, United States, before becoming a deadpooled company. The company focused on reimagining database technology to address the performance, speed, and scalability limitations of existing systems, particularly in the context of big data.

Thomas Hazel, a serial entrepreneur with a background in communication, virtualization, and database science, served as the Founder and CTO. His journey with the company began from an epiphany while working on a data-intensive project where the database performance was a significant bottleneck. This led him to reinvent the underlying science of databases. Hazel has a history of innovation, holding several patents in distributed processes and virtualization, and even sold a database he created to Oracle. His experience as Chief Architect at startups like Akiban and Virtual Iron further informed the technological direction of Deep Information Sciences.

The company's core product was the Deep Engine, a NewSQL storage engine designed as an alternative to traditional SQL storage engines like InnoDB. This was later marketed as deepSQL, a MySQL-compliant adaptive database that utilized machine learning for autonomic self-tuning. The technology was engineered to be resource-aware, adapting to the underlying hardware to maximize efficiency and performance in physical, virtual, and cloud environments. Key features included Hybrid Transactional and Analytical Processing (HTAP), the ability to store both structured and unstructured data, and both scale-up and scale-out models. The engine was based on a proprietary approach called Continuously Adaptive Sequential Summarization of Information (CASSI). The primary benefit for clients, which included service providers and enterprises, was a significant increase in speed and the ability to handle extreme concurrency without manual intervention, which was particularly valuable for the on-demand economy.

Deep Information Sciences secured a total of $18 million in funding over two Series A rounds. The first round was on April 2, 2013, and the second, which raised $8 million, was on March 16, 2015. The funding rounds were led by investors such as Stage 1 Ventures and Sigma Prime Ventures. This capital was intended to fuel the growth and market presence of the Deep Engine.