ValueWalk’s Q&A session with Ruggero Gramatica, the CEO of Yewno. In this interview he discusses Yewno’s goal, focusing on Fintech, if Yewno is meant for institutional or retail investors, offering derivative data feeds to Yahoo Finance, and using the using the knowledge graph.
Can you tell us about your background?
- Over 20 years of experience in startups and company turnarounds
- Contributed to the growth and expansion of 5 successful start-ups both in the United States and across Europe
- Operated successfully in both blue chip and early stage environments in Technology and Biotech sectors
- Bring strong technology background, financial, strategy and quantitative analysis skills to roles
- PhD in Applied Mathematics from King’s College London
- MBA from the University of Chicago Booth School of Business
- BS English from Politecnico di Milano University
Can you tell us about your Yewno?
The company was founded by Ruggero Gramatica, a seasoned executive with a vast track record in developing high performance teams, achieving successful exits for investors, and deep operational and management expertise.
Yewno (pronounced You-know) is a Silicon Valley start-up it was founded upon the intuition that Information Economy, which has been pivotal across many industries for decades, was about to shift towards the new emerging Knowledge Economy. That is because the overwhelming amount of information produced every day, fragmented and dispersed - cannot be handled by humans without new data framework based on emerging AI based technology.
At the base of its technology Yewno has created a proprietary Knowledge Graph framework and an inference engine with innumerable applications. It can be applied to a multitude of diverse fields such as: finance, economics, life sciences, law, education, marketing and general research.
Yewno’s engine has the capability to quickly and proficiently analyze broad sets of public and private information sources and instantly find inferences and interrelationships via its leading-edge computational semantics, graph-theoretical models and deep learning, as well as quantitative analytics that hunt for emerging patterns and connections across huge domains of structured and unstructured data sources.
Yewno’s solutions are able actionable knowledge from the ever-increasing amount of information and help to understand and make decisions better and faster than humanly possible.
Yewno’s goal is to become the foundation of research and decision making in finance, academia, risk assessment, intelligence, strategic marketing and consumer choices.
Fintech is a crowded space what made you decide to focus on your current offerings?
The sheer amount of information available to analysts, portfolio managers or investors in general has become overwhelming. And not only there is too much information but also the diversity of data does not help identifying non-obvious relationships that are cause or effect of economic or financial events.
Fintech is definitely undergoing a transformation which involves a) dealing with Alternative Data (i.e. the combination of data sets from diverse sources and domain which once correlated bring new insights) other than traditional fundamental data pertaining only companies or sectors information, b) the adoption of Machine Learning (or in general A.I.) solutions that can handle this new data framework and hunt for hidden signals. More generally Fintech is opening a new way of extract knowledge from information. We believe that Yewno is well suited to provide an edge to investors because of its ability to process hundreds of millions of data points via its dynamic knowledge graph and allow faster and better understanding of complex data.
Did you build the product around demands or more around what you were good at?
The product is the results of a long R&D phase with the precise goal to resolve the issues connected to the transformation of large amount of information into knowledge.
Meant for institutional or retail investors?
Yewno Finance serves clients spanning from large institutional firm to retail: from ETFs issuers , Hedge Funds, portfolio managers to RIAs who are seeking the ability to understand faster and better or to extract those insights that are hidden because of the large, dispersed amount of information available today.
Can you tell us how you define Fintech?
Fintech is a technology sector whose aim is to provide infrastructure / solutions for financial community.
Is anyone going to build the Bloomberg terminal killer?
Bloomberg has a very large consolidated platform which will remain the standard for long time, however new players are coming out with new technology and solutions which are quite advanced in extracting new derived data and signals better than traditional vendors.
You are now working with Yahoo Finance can you tell us about that?
We are offering derivative data feeds to Yahoo Finance through Nasdaq Analytic Hub whom we have a partnership with in providing alternative data feeds.
There is a ton of hype about AI can you define the term for us?
Unfortunately AI has become an abused term. AI is a general term that defines the use of advance technology in the attempt to replicate a form of intelligence mimicking the one of humans. However, it is often used without context - it is like asking your doctor what he does for living and he answers "I'm doing medicine....". AI should be conjugated in terms of what aspect of intelligent framework is used and its context. We often prefer the term Augmented Intelligence to signify the ability of new algorithmic framework to augment the human ability to understand/solve complex problems.
Same with Alternative data what is and isn’t it?
As mentioned above Alternative Data is a new form of data sets from diverse sources and domains which once correlated bring new insights versus other than traditional fundamental data pertaining only companies or sectors information.
How are you using the knowledge graph?
Yewno has the ability to read, understand, extract concepts from billions of structured and unstructured sources and across languages and correlate them into a virtually infinite, integrated, dynamic Knowledge Graph.
A true KG is a data framework which correlated such huge amount of unstructured data points and creates (not just map) relationships that are either factual or dynamically inferred. Also, a KG is something which constantly changes, therefore allowing smart algorithm to extract changes of its constituent over time. This framework cannot be confused with other solutions which just map existing structured database into a graph data structure.
You work on extracting data from many sources - do you need to license the info from them? Or does it fall under fair use? I would think if the former would be cost prohibitive?
It is a mix of those. We do not scrape the internet because we care about quality of data - some sources are available through free license other are paid for. The important thing is to never compromise with the quality of sources and keep on enriching the knowldge graph with as many as data as possible from many domains of knowledge.