# Rogo Financial Model Bespoke generative AI platform for financial services firms - connects firm knowledge to finance-specific LLMs to accelerate analyst and senior team workflows. - Canonical: https://finamodel.com/startups/rogo - Excel download: https://finamodel.com/startup-models/rogo.xlsx - Category: Fintech - Model type: SaaS ARR / Valuation - Funding round: Seed - Funding: $7m - Founded: 2024 - Geography: Not in deck (US-centric examples: SEC filings, EDGAR, US equities). - Customer: B2B ## About the company Rogo is a bespoke generative-AI platform for financial-services firms, connecting proprietary institutional knowledge with finance-specific language models. It is designed to accelerate analyst and senior workflows without forcing sensitive information into generic tools. The company’s team and target market point to an enterprise-first sales motion into investment banks, asset managers, and other financial institutions. Commercially, the platform is expected to be sold as an annual firm or seat-based software subscription. The model should forecast target firms, sales cycle, deployed seats, annual seat price, expansion into new teams, and logo churn. Implementation and security costs should be separated from recurring ARR, while customer concentration and renewal timing remain visible in the enterprise forecast. ## 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 Rogo is a chat-based AI research platform that ingests both a firm's proprietary data (internal notes, precedent slides, Excel models, internal workflows) and 100m+ external sources (earnings transcripts, SEC filings, market data, news, web) and surfaces answers with cited sources. Key differentiators vs. general-purpose LLMs (ChatGPT): - Finance-specific context and data - not a generic model. - Source citations on every answer - auditable outputs. - Integration into existing firm workflows (Excel, PowerPoint, internal systems). Three use-case tiers: 1. Optimize junior analyst time (routine research, benchmarking). 2. Empower senior team self-service (strategic questions, competitive analysis). 3. End-to-end workflow automation (PIBs, PowerPoint pages, automated earnings alerts). ## Revenue model - Enterprise SaaS subscription sold per seat or per firm, likely annual contract. Rationale: standard for AI tools deployed inside financial services firms with proprietary data integrations; consistent with team background (Citadel, Lazard, JP Morgan, Barclays, Jefferies pedigree suggests enterprise-first go-to-market). - Potential professional services / implementation component for bespoke deployments. Rationale: deck emphasizes "bespoke" and firm-specific configuration. ## Competition / moat Moat framing (explicit in deck): - Team composition: "almost entirely former investors, bankers and AI researchers" - domain expertise baked into product design. - Finance-specific LLMs vs. general-purpose models (ChatGPT, Copilot). - Data integration with firm's proprietary knowledge (precedent slides, internal models, workflows) creates switching cost. Named competitors (implied): ChatGPT / GPT-4 used as benchmark foil. No direct fintech AI competitors named. Notable team backgrounds: Citadel, Lazard, MongoDB, AWS, Sentieo, Jefferies, Barclays, GGHC, AlphaSense, JP Morgan. ## Team & funding ask / use of funds - Team: AI researchers with financial services backgrounds (see slide 2 logos). Names not extracted from text; one user shown in product demo is "Chris Zachary." - Contact: team@rogodata.com --- ## Recommended financial model - **Archetype + why:** B2B SaaS ARR model. Rogo sells an enterprise AI platform on subscription to financial services firms. The right frame is ARR waterfall (new ARR, expansion, churn, net new ARR) with seat-based or firm-count drivers. No M&A or SPAC elements present. - **Forecast horizon & granularity:** 3 years (2024–2026), monthly for Year 1 (cash management visibility), quarterly for Years 2–3. Annual summary output. - **Key drivers & assumptions:** - Starting customer count: 5–15 firms at model open; rationale: early-growth stage, enterprise sales cycle, no traction disclosed. - New logos per quarter: 3–8; rationale: enterprise fintech sales velocity benchmarks; will be the primary sensitivity lever. - Average contract value (ACV): $50k–$150k/year per firm; rationale: comparable enterprise AI tools (AlphaSense ~$50k+, Sentieo similar range) for mid-market financial firms; large bank deals could be multiples higher. - Seat-based expansion within firms: net revenue retention 110–130%; rationale: platform expands from analyst teams to senior coverage; consistent with finance AI comps. - Gross margin: 70–80%; rationale: SaaS with LLM inference cost (API / compute) as COGS; inference costs will compress margin vs. pure SaaS. - S&M as % revenue: 40–60% in early years; rationale: enterprise fintech requires direct sales into compliance-sensitive institutions. - R&D as % revenue: 30–40%; rationale: AI model fine-tuning, data pipeline, and finance-specific LLM development are core ongoing costs. - G&A as % revenue: 10–15%. - Headcount: model separately; hiring plan drives opex more than revenue percentages at early stage. - Churn (gross logo): 5–10% annually; rationale: enterprise fintech tends to be sticky once integrated into workflows; switching cost is high given proprietary data ingestion. - **Scenarios (Base / Bull / Bear - which variables flex):** - Base: 5 new logos/quarter, ACV $80k, NRR 115%, gross margin 75%. - Bull: 10 new logos/quarter, ACV $120k, NRR 125% (significant seat expansion at large banks), gross margin 78%. - Bear: 2 new logos/quarter, ACV $50k, NRR 105% (limited expansion, budget scrutiny), gross margin 68% (higher inference costs). - **Required sheets / outputs:** - Assumptions (all drivers, scenario toggle). - ARR Waterfall (beginning ARR, new ARR, expansion, churn, ending ARR by period). - P&L (revenue, COGS → gross profit, S&M, R&D, G&A → EBITDA, net income). - Headcount & opex build. - Cash & runway (burn, capital raise timing if applicable). - Dashboard: ARR, NRR, gross margin %, burn, months of runway. ## Frequently asked questions ### Is the Rogo financial model free? Yes. The Rogo 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.