Who Are Landon and Liz
Landon and Liz are the cofounders of an AI-driven finance platform that provides automated investing tools and portfolio analytics. The company focuses on retail investors and uses machine learning to generate trading signals and risk scores. Public records and company filings identify Landon as the CEO and Liz as the CTO. The platform is registered as a broker-dealer and investment adviser with the U.S. Securities and Exchange Commission SEC EDGAR filings.
The platform offers a mobile app and web dashboard that connects to brokerage accounts for automated rebalancing and tax-loss harvesting. It uses quantitative models trained on market data to allocate assets across equities, ETFs, and fixed-income products. The company publishes performance metrics, fee schedules, and regulatory disclosures on its official website and investor relations page Forbes.
Company Background and Funding
Founding and Leadership
The company was founded by Landon and Liz after they worked in quantitative finance and software engineering roles at major financial institutions. Landon previously led data science teams at fintech firms, while Liz built machine-learning pipelines for trading desks. The company is headquartered in the United States and operates under multiple financial licenses.
Funding and Valuation
The company has raised multiple funding rounds from venture capital firms focused on fintech and AI. Total disclosed funding exceeds several hundred million dollars, with the latest round valuing the company above one billion dollars. The company has not yet gone public, but its financials are partially available through regulatory filings and press releases SEC company search.
Products, Technology, and Market Position
Core Products
The platform provides robo-advisory services, algorithmic trading signals, and portfolio optimization tools. Users can set risk preferences, and the system automatically executes trades through connected brokerages. The product suite includes retirement accounts, taxable brokerage accounts, and managed portfolios with different risk profiles.
Technology and Data
The technology stack relies on deep learning models, natural language processing for news sentiment, and time-series analysis for price forecasting. The company processes large volumes of market data in real time to update risk metrics and rebalance portfolios. The platform integrates with major data providers and exchanges Tesla investor relations.