Bringing AI to Fixed Income

We build AI foundation models to address the $100T fixed income market, beginning with real-time, accurate pricing for munis.


What we are is a financial technology firm based in Seattle and New York that provides accurate real-time pricing for over one million municipal bonds, enabled by the latest advances in machine learning. Our artificial intelligence models provide continuous understanding of the market for munis, enabling all market participants to operate with greater speed, visibility, confidence, and efficiency.

How we do it

Our technology creates profit opportunities by enabling users to evaluate buying and selling opportunities systematically and quickly. This allows clients to take advantage of market inefficiencies and automate trades with real time, accurate, and fast pricing. Increasingly, the determination of market actions, both on the sell side and buy side, is done by algorithms. Our technology is a crucial enabler of this trend for municipal bonds.

Whom we help

1) Traders: price entire BWs list in seconds to create accurate rich cheap analysis. 2) SMA, Asset Managers: accurately price specific odd lots size help SMAs when evaluating offerings. 3) Quants: automate trades with real time, accurate, and fast pricing. 4) Compliance: using accurate price evaluation algorithms help with risk management.

Product Highlights

AI-powered real-time pricing streams for municipal bonds give investors and traders an unparalleled advantage in the market. Our product use muni's ETFs to track the market tone in real-time, and advanced algorithms that continuously improve the accuracy of our pricing capabilities. Our product can incorporate decision theory for optimized automated trading and additional machine learning for optimized hedging, ensuring that we remain at the forefront of the industry.

By using deep neural networks, which outperform decision tree methods and other older methods, we deliver accurate results throughout the trading day, which are far more useful than the end-of-day prices provided by existing services. Estimated prices and predicted yield-to-worst (YTW) can be consumed via API, via in-house applications, and/or via spreadsheets. All software is cloud-based and highly scalable, so integration with existing order-management systems and trade processing systems is straightforward.


Gil Shulman

Founder & CEO

Prior to starting, Gil spent six years at Microsoft AI and in the Amazon core machine learning team, where he drove initiatives to apply state-of-the-art artificial intelligence techniques to real-world applications with several hundred million users. Earlier, Gil founded Amobee, the world’s first mobile advertising company, sold to Sing-Tel for $324mm. He started his career as a software engineer and has an MBA from the Wharton School, University of Pennsylvania.

Stephen Winterstein

Senior Advisor

Stephen Winterstein is the founder and managing partner of SP Winterstein & Associates. The firm advises dealers and buyside firms on municipal fixed income data and technology procurement, vendor engagement, workflow, and market structure. With over 35 years experience in municipal fixed income SMA and mutual fund management, electronic trading, and fintech, he has worked in numerous roles, most recently as head of capital markets at Alpha Ledger Technologies and head of municipal fixed income at MarketAxess.

Charles Elkan

Founder & Scientific Advisor

Charles was most recently a managing director at Goldman Sachs in New York, where he was the global head of machine learning. Previously he led the central machine learning organization for Amazon in Seattle, and spent many years as a professor of computer science and artificial intelligence at the University of California, San Diego. He has a Ph.D. from Cornell University in computer science and an undergraduate degree in mathematics from Cambridge University.

Mitas Ray

Senior ML Scientist

Prior to joining, Mitas was a researcher at the University of Washington in Seattle, working on machine learning methods for sequential decision making and decision-dependent optimization. Mitas holds a M.S. from the University of Washington. Immediately after earning his B.S. in computer science from the University of California, Berkeley, Mitas was appointed as a lecturer there.

Jesse Watson

Data & Research

Jesse came to from Oberlin College (Ohio) where he was a professor teaching courses on technology in East Asia. He has also taught at Smith College (Massachusetts) and holds a Ph.D. from the University of California, Berkeley, an M.A. from Peking University (China) and a B.A. from Wesleyan University (Connecticut). Jesse has been a Fulbright Scholar of the US Department of State.

Isaac Lim

ML Scientist

Isaac has extensive experience in both machine learning and finance. He earned his M.S. in management science and engineering from Columbia University in 2021. Previously, he earned a B.S. in economics with first class honors at University College, London. Isaac has worked on applied machine learning projects for the Ministry of Finance in Singapore and the luxury group LVMH.

Myles Schoonover

Product Manager

Prior to joining, Myles was a professor at Lee University. Previously Myles had been a researcher at the Université de Lorraine (Metz, France) as well as at Oxford University (Oriel College). Myles will defend his Ph.D. dissertation at the University of Groningen (Netherlands) in 2024, and holds an M.A. from Yale University.

Ahmad Shayaan

Senior ML Scientist

Ahmad Shayaan joined from Columbia University, where he earned his M.S. in financial engineering. He has previous research experience in artificial intelligence and at hedge funds. He also holds M.S. and B.S. degrees in computer science from IIIT Bangalore (India).

Jay Alpert

Executive Director of Business Development

Jay Alpert’s career spans over 40 years on Wall Street. He managed trading and underwriting for Paine Webber, before becoming an owner and partner of Taylor Byrne, Inc. He then led the Municipal Bond Department of M.R. Beal (as executive Vice-President). At Sterne, Agree & Leach—whose New York office he launched—Jay was branch manager of trading and sales, where he also established a public finance platform. A past board member of FINRA’s Financial Technology and Compliance Committee, Jay is a graduate of Brooklyn College (CUNY) and the New York Institute of Finance; he is a veteran of the US Marine Corps.

JR Rieger

Muni Advisor

A 37-year bond market veteran, JR is currently owner of the Rieger Report® LLC, and is a Lecturer at Clemson University’s Wilbur O. and Ann Powers School of Business. Previously, JR was the Managing Director and Global Head of Fixed Income Indices at S&P Dow Jones Indices where he and his team developed and launched a global suite of debt market indices resulting in investable index products in 12 countries totaling over $46 billion in assets under management. Under JR’s leadership, S&P launched the Municipal Series (maturing indices), the S&P 500 Bond Index family, the S&P Green Bond Index family and the first ever factor-based U.S. high yield corporate bond index. JR presented on the bond markets around the globe and represented S&P as a subject matter expert on the bond markets with the financial media in both print and television including CNBC and Bloomberg News. Prior to joining S&P Dow Jones Indices in 2007, JR was the Global Desk Head of Securities Evaluations at S&P Securities Evaluations (FKA J.J. Kenny).

Contact Us

Click here to request a demonstration of our technology. You can also email our Municipal Markets Product Manager Myles at, or our CEO Gil at

We are always looking for world-class software engineering, financial, and machine learning talent to join our growing team. If you are interested, email us at