The Capital Allocator
Tells you which bets have the highest ROI before you make them
How It Works
When a capital allocation decision is on the table (a new hire, a marketing campaign, a technology purchase, or a market expansion), the Capital Allocator pulls historical performance data and builds scenario models. It generates conservative, moderate, and aggressive projections for each option, surfaces the key assumptions driving each scenario, and presents the analysis to a decision-maker for review. The human decides, with full context rather than a gut feel.
Step-by-Step Flow
Connect your financial and operational data sources
Define the decision framework: what types of investments to model
Submit an allocation decision for analysis
AI builds scenario models using historical data and comparable benchmarks
Scenarios presented with key assumptions surfaced for review
Decision-maker approves the allocation plan with full context
Best For
- Business owners and operators making $50K–$500K capital decisions
- Companies that want more rigor in budget decisions without a full finance team
- Leadership teams that trust data over intuition but lack the tools to generate it
This is customized for your business.
Every node, tool, and logic path shown here gets adapted to your team structure, your CRM, and your existing workflows. What you see is the proven pattern. What we build together is built specifically for you.
Implementation Notes
Decision types modeled include new hires, marketing channel investments, technology platform purchases, market expansion, and physical infrastructure. For each decision, the model pulls three categories of data: internal performance benchmarks from your own financial history (revenue per head, customer acquisition cost, payback period), comparable industry benchmarks from public sources, and prior decisions of the same type with their outcomes where logged. Three scenarios generate for each option: conservative using 25th percentile assumptions, moderate using 50th percentile, and aggressive using 75th percentile. Each scenario shows projected impact on revenue, margin, cash runway, and payback period at 6, 12, and 24 months. Key assumptions are surfaced explicitly in the output rather than buried in the model. The decision package delivers as a structured document in Google Docs or Notion with a one-page summary and a full model appendix. The decision-maker logs their final choice and reasoning, which is stored for future model calibration. Prerequisites: at least 12 months of financial history, a defined chart of accounts, and a list of decision types you want the model to handle.