# Vise AI Financial Model AI-powered outsourced portfolio construction and management platform for independent financial advisors and RIAs - Canonical: https://finamodel.com/startups/vise-ai - Excel download: https://finamodel.com/startup-models/vise-ai.xlsx - Category: Fintech - Model type: SaaS ARR / Valuation - Funding round: Seed - Funding: $3.5M - Founded: 2019 - Geography: United States (implied by RIA/broker-dealer references) - Customer: B2C ## About the company Vise is an AI-powered outsourced portfolio-construction and management platform for independent financial advisors and RIAs. It gives advisors institutional-style portfolio tools while letting them retain their own client relationships and advisory proposition. The company charges an all-inclusive fee averaging 0.25% of assets managed on the platform, replacing parts of the traditional TAMP stack. The commercial model is therefore driven by advisor onboarding and client assets rather than software seats. The model should forecast RIA firms, advisors per firm, accounts onboarded, starting AUM, net flows, market appreciation, and the 25-basis-point fee. Advisor retention, client transfers, and servicing costs are the primary sensitivities in an AUM-based revenue build. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition Vise AI is an outsourced portfolio construction and management tool targeting independent RIAs and regional institutions. It replaces traditional TAMP + manager + clearing fee stacks with a single all-inclusive 0.25% AUM fee. Three core pillars: - **Automation** - automated portfolio construction and management, saving advisor time - **Intelligence** - NLP-driven portfolio justification ("why" behind portfolio decisions), talking points for advisors - **Customization** - stock-based, AI-managed portfolios tailored to individual clients Technology stack: ML/recurrent neural networks for portfolio construction, NLP for reading analyst reports and SEC filings, smart tax-based rebalancing, white-label interface integrated into firm's trading platform. ## Market Target segment: Independent RIAs & Regional Institutions - AUM in target segment: $10T - TAM stated: $30B (total addressable market - interpreted as revenue TAM on $10T AUM) - Target segment AUM growth: 16.7% last year - Average firm discretionary assets: $285M Adjacent segments (not targeted): - D2C Robo-Advisors: <$20B AUM, $28k avg account size - Institutional Wealth Management: $70T AUM, 8.5% last year growth, <12 institutions controlling market Market tailwinds: - 77%+ RIAs cite automation and digital capabilities as biggest need in next 5 years - 61%+ advisors spend at least 40% of time on investment management activities - 78%+ clients cite technology use as key factor in choosing or leaving their advisor ## Revenue model - **Fee structure**: AUM-based fee averaging **0.25%** on total assets managed on platform - all-inclusive (covers trading costs and Vise AI margin) - **Client fee pass-through**: Advisor charges their own separate ~1% advisory fee on top; Vise AI's 0.25% replaces the industry-standard TAMP stack (0.75% manager + 0.10% TAMP + 0.05% clearing = 0.90%) - **Channel**: Direct B2B sales to RIA firms; no D2C - **Unit**: AUM on platform → revenue = AUM × 0.25% Industry standard comparison: - Advisor fee: 1% (same in both) - Industry overhead: 0.75% manager + 0.10% TAMP + 0.05% clearing = 0.90% total - Vise AI overhead: 0.25% all-inclusive → cost saving of 0.65% to advisor or client ## Competition / moat Competitive positioning framed via Venn diagram: - Robo-advisors: save time + save money, but no customization - Equity managers: customization + save money, but not time-efficient - In-house investments: save time + customization, but expensive - Vise AI claims all three ("the holy grail") Moat: proprietary ML models (RNN for return prediction, NLP for research ingestion), integrated platform design, low-cost structure replacing legacy TAMP stack. Named competitors: not explicitly listed; category competitors implied (Robo-advisors, TAMPs, equity managers). ## Team & funding ask / use of funds **Team**: - Samir Vasavada - Co-Founder & CEO; startup/ops background, consulted Microsoft, BCG, Deutsche Bank on FinTech/AI - Runik Mehrotra - Co-Founder & CTO; ML/AI research, consulted MassMutual, RBC, Blackstone, Deutsche Bank, Morgan Stanley; Wharton/Penn Engineering - Dr. Marc Ettlinger Ph.D. - AI/ML Engineer; Google NLP team director, UC Berkeley PhD - Robert Owen - MD & CCO; RIA/advisor 10+ years, built SMA/UMA platform acquired by E-Trade - Dr. Joshua Woodruff Ph.D. - Lead Data Scientist; VP Morgan Stanley, Yale/UT Austin PhD, IBM modeling - Scott Winters - Advisor; CEO Financial Gravity, founder EQIS (large TAMP for RIAs) - Dr. Joerg Osterrieder Ph.D. - ML Scientific Advisor; Goldman Sachs, quant/portfolio researcher, PhD Financial Mathematics - Jon Xu - Advisor; co-founder FutureAdvisor (acquired by BlackRock) --- ## Recommended financial model - **Archetype + why**: AUM-growth / revenue-from-AUM model. Revenue is purely a function of assets on platform × fee rate (0.25%). This is structurally identical to a TAMP or RIA revenue model - not SaaS ARR, no seat fees. Core driver is AUM ramp, not customer count alone. - **Forecast horizon & granularity**: 5 years; monthly for Year 1 (ramp is nonlinear), quarterly for Years 2–5. Key inflection to model is first advisor onboarding and AUM accumulation curve. - **Key drivers & assumptions**: - Fee rate: **0.25% of AUM** - blended, all-inclusive - Target segment AUM pool: **$10T**; average firm discretionary AUM: **$285M** - Number of advisor firms onboarded per quarter - start with 2–5 in Y1, ramp; no deck data - Average AUM per firm onboarded: **$285M** used as seed; bear case $50M (small RIA), bull $285M+ - AUM growth per firm per year: **16.7%** (target segment growth rate used as proxy organic AUM growth for existing clients) - Advisor fee (pass-through, not Vise revenue): 1% - for context only - Platform revenue = AUM on platform × 0.25% / 12 (monthly accrual) - G&A: ~15–20% of revenue, declining at scale - **Scenarios (Base / Bull / Bear - which variables flex)**: - **Bear**: Slow RIA adoption (2–3 firms/quarter Y1–Y2), lower avg AUM per firm ($50M), fee compression to 0.20% by Y3 - **Base**: 5 firms/quarter Y1 ramping to 15+/quarter Y3, avg AUM $150M/firm, fee holds at 0.25% - **Bull**: RIA market opens fast (15+ firms/quarter Y2), avg AUM $285M, organic AUM growth 16.7%/year - **Required sheets / outputs**: 1. Assumptions - fee rate, firms onboarded per period, avg AUM/firm, AUM growth rate, headcount, COGS%, S&M% 2. AUM Bridge - opening AUM + new firm AUM + organic growth − churn/withdrawals = closing AUM 3. Revenue - AUM × 0.25% annualized 4. P&L (Income Statement) - Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA, Net Income 5. Headcount & Compensation - by department 6. Cash Flow & Runway - burn rate, cash balance, months of runway 7. Scenario toggle (Base / Bull / Bear) 8. KPI Summary - AUM on platform, # advisor firms, revenue, gross margin %, burn, runway ## Frequently asked questions ### Is the Vise AI financial model free? Yes. The Vise AI model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.