Unified Financial Insights:
Predictive Power in Numbers & Narratives
Euclid AI
EuclidAI harnesses the power of Generative AI and Machine Learning to extract meaningful insights from unstructured data sources such as SEC filings, PRNewswire, Earnings Calls, and more. Our aim is to provide precise financial predictions coupled with thorough explanations. We pride ourselves on delivering a comprehensive, real-time, and interactive stock analysis experience. EuclidAI ensures full coverage of all publicly traded stocks. Engage in direct conversations with your dedicated AI analyst.
Euclid Analyst
Our first product is an AI-powered forecaster of corporate earnings. We use SEC 10-Q and 10-K filings to extract text signals. These signals feed into our machine-learning model to predict future earnings.
Architecture
We build a combined machine-learning (ML) and large language model (LLM) process which extracts signals from text sources, economic indicators, and accounting filings. These data allow us to forecast corporate quarterly earnings as well as other variables.
For our forecast of corporate earnings, at a high level, here is what we do:
Product Clarity
We use multiple sources of public data, both structured and unstructured, as the basis for our predictions. We use XBRL accounting data that every publicly traded company files with the SEC. We also use earnings conference calls, PR Newswire, tweets, public macro-economic variables, and other sources.
We use an innovative and customized combination of machine learning techniques to synthesize insights from the structured and unstructured elements to predict financial performance metrics for each analyzed company for several quarters into the future.
Many publicly traded stocks are not followed by human analysts. Euclid quickly creates analysis reports for every company that files an SEC report regardless of whether any other analyst reports are available. Also, Euclid reports are always up-to-date, taking in new information as it arrives, be it breaking news, conference calls, or new SEC filings.
We wrap the model into a real-time interactive expert stock analyst at your fingertips. Ask your own questions about any publicly traded stock and get customized insights back from the Euclid model. Ask ‘what-if’ questions and discuss the results.
White Papers
SEC accounting data can be confusing. See the white paper on SP 500 XBRL Tagging Practices by Kim Hoots
About Us
Debarag Banerjee
Debarag is an accomplished AI entrepreneur and technologist with over 25 years of experience pioneering cutting-edge innovations. He holds a PhD from Stanford University, has 15 issued patents, and led the development of hundreds of machine learning systems at startups and Fortune 500 companies that extracted over $1 billion in incremental value.
Debarag has extensive expertise architecting scalable cloud-based AI platforms leveraging advanced techniques like deep learning and natural language processing. He excels at guiding cross-functional teams to build industry-defining products that disrupt traditional ways of solving problems. In addition to serving as an executive leader overseeing AI and Data Science functions in several multi-billion dollar public companies, he founded two algorithm-driven startups, Avnera and WiViu, both of which were successfully acquired by public companies. Currently, as co-founder of EuclidAI, he is developing proprietary AI algorithms that enable unparalleled accuracy in financial forecasting.
Efraim was a founding member and the director of computational macroeconomics at the Penn Wharton Budget Model at the University of Pennsylvania’s Wharton School from 2015 to 2023. His analyses were frequently cited and he appeared on major media, including the Wall St. Journal, New York Times, AP, Bloomberg, Yahoo! Finance, major broadcast and cable networks, and others. He published on topics in corporate and individual tax policy; government debt and fiscal policy; social programs, Social Security, universal pre-K; effects of school shutdowns during COVID; immigration; trade and tariffs; and others. His work on credit cards and interchange fees was presented to the U.S. Congress and cited in the New Yorker.
Efraim held senior information technology roles in finance organizations, including AXA Financial, Sanford Bernstein (now AllianceBernstein), and NASD (now FINRA). He earned a PhD in Economics from the University of Pennsylvania, a MS in Electrical Engineering from the University of Maryland, College Park, and a BS in Mathematics from Georgetown University.
Efraim Berkovich
Efraim Berkovich
Efraim held senior information technology roles in finance organizations, including AXA Financial, Sanford Bernstein (now AllianceBernstein), and NASD (now FINRA). He earned a PhD in Economics from the University of Pennsylvania, a MS in Electrical Engineering from the University of Maryland, College Park, and a BS in Mathematics from Georgetown University.
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