FAQ
Frequently asked questions
Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.
Can an AI really build a financial model that investors will take seriously?
The model is only as good as the assumptions and the data feeding it. AI CFO handles the structure, the formulas, the data plumbing, and the error-checking flawlessly — the parts where human analysts make 90% of their mistakes. The assumptions (growth rate, hiring plan, pricing changes) are still yours — you tell the AI your assumptions, and it builds the model, stress-tests it, and flags inconsistencies ('your revenue projection assumes 15% monthly growth but your CAC is also rising 20% — these conflict'). Investors care about assumptions being well-reasoned, not whether a human typed the SUM formulas. Tycoon founders have raised seed and Series A rounds with AI-built models; the key is the founder owns the assumptions.
How is this different from Finmark, Pry, or Runway?
Those are financial planning tools — they give you a dashboard to build models yourself. Tycoon's AI CFO builds and maintains the model for you. Finmark will let you create a scenario; Tycoon will suggest the scenario, run it, and tell you the runway impact in chat before you ask. The difference is the same across all Tycoon use cases: those tools are software you operate; Tycoon is an AI employee that operates the software. For founders who aren't finance-native, having the model built and explained in plain English is worth more than a more powerful modeling interface.
What about industry-specific models — SaaS metrics, marketplace GMV, hardware COGS?
AI CFO adapts the model structure to your business model. SaaS gets ARR/MRR waterfalls, churn cohorts, LTV:CAC, net revenue retention, magic number. Marketplaces get GMV, take rate, liquidity metrics, supply/demand balance. Hardware gets COGS breakdown, inventory turns, manufacturing overhead allocation. You tell it your business model and which metrics matter; it builds the right model structure. The AI understands financial modeling patterns across verticals — it's not limited to one template.
Can it handle multi-entity or international consolidation?
Yes, at the standard complexity level for companies under $50M revenue. AI CFO can maintain separate models per entity (US parent, UK subsidiary, Singapore subsidiary) with intercompany eliminations and consolidated reporting. For complex transfer pricing, multi-currency hedging, or 20+ entity structures with intercompany debt — that's beyond current scope and you'd still want a human CFO or controller for consolidation review. But for the typical startup with a US entity plus 1-2 international subsidiaries, the AI handles entity-level plus consolidated views cleanly.
How does the AI learn our specific unit economics over time?
AI CFO starts with your historical data to establish baselines. Month 1 it projects based on industry benchmarks adjusted for your size. By month 3, it's learned your actual patterns: your CAC payback is 8 months, not 6; your churn spikes in January; your enterprise deals close 2× slower than self-serve. Every new month of data tightens the projections. The model gets more accurate the longer it runs — it's a learning system, not a one-time build. Founders who start using it 3 months before a fundraise get materially better models than founders who spin it up the week before.