When I first started the All Things Interesting Podcast, my first guest was Kasian Franks who is the technical co-founder of the San Francisco based startup, Vectorspace AI. Also known by its ticker symbol (IDEX: VXV), Vectorspace AI uses Natural Language Understanding or NLU for short to produce feature vectors. These feature vectors serve the purpose of creating datasets which are correlation matrices designed to detect and extract hidden relationships across a spectrum of industries including the life sciences and financial markets.
As Kasian Franks put it, Vectorspace AI got its start going way back to the late 90’s and early 2000’s when his work at the time was focused on pattern matching algorithms which spawned early bio-mimetic search engine startups including SeeqPod, and Mimvi.
About Vector Space AI
Vector Space AI is a unique platform that aims to drive quicker and higher quality results in data science by leveraging context controlled Natural Language processing and feature engineering. Feature vectors are constituted into smart basket data sets which are powered with artificial intelligence and machine learning algorithms to drive better solutions for generating alpha for the world’s leading funds, research groups, institutions, and vendors. At the core of the platform, Vectorspace AI is backed by multiple patents by the team in collaboration with Lawrence Berkeley National Laboratory in the area of Natural Language Processing/Understanding (NLP/NLU) and Machine Learning (ML).
On the front of blockchain, the team seeks to enable companies to onboard with VXV based on its API service to allow companies to conduct active real-time data analysis. The company provides a multi-tiered subscription service which provides varied degrees of services and high-value datasets based on VXV token holdings. Further augmenting the platform, the company created a data provenance pipeline (DPP) to allow companies to track their data back to its source and to understand its reliability.
From my discussions with Vectorspace AI and research into the company and its history, VXV has shaped an interesting niche for itself in the market. With use cases spanning from pharmaceutical research to generating alpha in the financial markets, the platform and token model offer a great deal of potential to institutions seeking to employ sophisticated and cutting-edge machine learning and AI solutions to their enterprise. With the recent vote of confidence by Elastic (NYSE: ESTC) through their article presenting the use of VXV data sets on their Canvas platform, the project is one keep an eye on.
For those interested in learning more about the history of Vectorspace AI, be sure to check out episode 1 of The All Things Interesting podcast in which we sit down to talk all thing data, machine learning, blockchain, and Elastic. Stay tuned for new posts