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An AI-driven investment management platform that allows users to design, back-test, and execute automated trading strategies across their existing brokerage accounts.
Automated Investment Management Cross Brokerage Account Aggregation AI Trading Strategy Formulation Quantitative Performance Backtesting Strategy Marketplace Monetization
Connects directly with major external brokerage accounts to manage assets centrally without moving raw capital Supports flexible configuration layers ranging from visual no-code builders up to full Python workspace IDE modules Leverages specialized AI data filters to audit multi-channel social sentiment and news telemetry inputs Provides risk guardrails by running strategies through a series of federal qualification and compliance review standards Builds secondary passive reward flows by allowing strategy authors to rent out custom automated logic scripts
Category: Business & Operations
Team Size: 2-10
Visit WebsiteSurmount AI is an advanced financial technology platform built to democratize algorithmic wealth management and quantitative portfolio strategies. The software aggregates distributed financial accounts into a unified command dashboard, allowing retail investors and financial advisors to run data-driven execution layers without changing custodians or initiating complex transfer processes. By integrating low-code, no-code, and native Python execution environments, it lets creators formulate, simulate, and backtest custom strategies across diverse asset classes like equities and crypto. The network also hosts an interactive marketplace where strategy creators can monetize successful performance models safely. Surmount AI bridges retail investment strategies with high-grade asset management infrastructure using cloud automation.
Surmount AI was founded in 2021 by quantitative finance developer Logan Weaver. Inspired to act after witnessing his grandmother being taken advantage of by a traditional human wealth manager who extracted high fees while providing stagnant value, Weaver set out to democratize institutional-grade software systems. He built an automated infrastructure capable of converting mathematical trading strategies into programmatic execution scripts, eventually scaling the platform to over 10,000 active monthly users out of San Francisco, California.