Vise AI Financial Model

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.

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

• 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

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:

• 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)

• 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