Pave Finance Raises $14M Seed to Launch AI Portfolio Management Platform for Advisors

Pave Finance Raises $14M to Roll Out AI Portfolio Management Platform for Advisors

Pave Finance closed a $14 million oversubscribed seed round to accelerate development and commercial rollout of its AI portfolio management software for investment advisors. The raise exceeded the initial $10 million target and included participation from former executives and board members of major US financial services firms.

The company's pitch is simple: advisors spend too much time on manual portfolio work. Pave says the average advisor loses roughly 18 hours per week to portfolio construction, rebalancing, and trading. The software automates those tasks while preserving individual client constraints.

What the Platform Does

Pave integrates an alpha scoring algorithm, an optimization engine, and a trading platform. It tracks 10,000+ publicly listed securities worldwide and builds portfolios around client risk tolerance, legacy positions, and tax considerations.

Machine learning and predictive analytics generate buy/sell recommendations and proposed allocations. Trades can be executed directly through integrations with Charles Schwab, Fidelity, and Interactive Brokers.

Adoption and Performance Data

The platform originated from quantitative models the team previously used to manage multi-billion-dollar portfolios. Pave reports that its standard model outperformed the S&P 500 by an average of 285 basis points per year over the last 15 years. Past performance is not indicative of future results.

Since launch, independent advisors using Pave collectively oversee more than 60,000 client accounts and over $18 billion in assets. CEO and co-founder Christopher Ainsworth said advisor adoption and asset flows signal the platform's momentum and will guide faster product expansion.

Leadership and Structure

The leadership team brings experience from US Trust, Deutsche Bank Private Wealth, SAC Capital, Merrill Lynch, and Morgan Stanley. Ainsworth previously led the west coast for Deutsche Bank Americas. Co-founder and chief risk officer Peter Corey traded macro at SAC Capital and ran derivative desks at Lehman Brothers and HSBC. Chief investment officer Steve Evans built the investment system at U.S. Trust and oversaw $7 billion in client assets.

Pave operates three units: Pave Labs (software), Pave Securities (broker-dealer), and Pave Investment Advisors (SEC-registered investment advisor). Revenue streams include software licensing, broker-dealer trading/spreads, and advisory fees. You can verify registrations via the SEC's IAPD database: adviserinfo.sec.gov.

Why This Matters for Advisors

Time is the scarce resource. If software can compress portfolio construction, tax-aware rebalancing, and trade execution into minutes, advisors can reallocate hours to client acquisition, planning, and deeper relationships-without adding headcount.

The key is whether the tool preserves your investment philosophy while improving consistency, tax outcomes, and operational accuracy. If it does, margins improve and service quality becomes more repeatable.

What to Evaluate Before Adopting

• Model transparency and governance: inputs, factor stability, drawdown history, and how recommendations change in stress.
• Tax logic: lot selection, wash sale handling, gains budgeting, household-level optimization.
• Integrations: custodians (Schwab, Fidelity, Interactive Brokers), OMS/EMS, CRM, and reporting vendors.
• Compliance and audit trail: rationale for trades, pre/post-trade checks, and documentation for the SEC and firm policies.
• Client personalization: IPS constraints, ESG exclusions, concentrated position hedging, and transition plans.
• Economics: software fees vs. time saved, trading costs and spreads through the broker-dealer, and overall impact on practice profitability.

Bottom Line

Advisors are under pressure to deliver personalization at scale while containing costs. Pave is betting that an integrated build-optimize-trade loop-backed by established quant models-will win that time back and standardize best practices across teams.

If you're building AI capability inside your practice, explore practical training and tool roundups here: AI tools for finance.