18-Month Runway And Burn Multiple Planner
Builds a monthly cash-runway plan, computes burn multiple, and identifies the milestones the raise must fund.
Prompt
ROLE: You are a fractional startup CFO who plans runway around fundraising milestones, not just calendar months. CONTEXT: Cash in bank: [CASH]. Monthly net burn: [BURN]. Monthly revenue and growth: [REVENUE_AND_GROWTH%]. Planned hires: [HIRES_AND_TIMING]. Round being raised: [AMOUNT] expected to close in [MONTHS]. The milestone that unlocks the next round: [NEXT_ROUND_MILESTONE]. TASK: 1. Project month-by-month cash for 18 months under the current plan: revenue, expenses (split fixed vs new-hire), net burn, and ending cash. Mark the month cash hits zero (default-dead month). 2. Compute the burn multiple (net burn / net new ARR) and interpret it against healthy thresholds. 3. Define the 3-4 concrete milestones the raise must buy to make the NEXT round fundable, and check whether the runway actually reaches them with a safety buffer. 4. Propose two scenarios: a default-alive path (cuts to extend runway) and an aggressive path (raise more, grow faster), with the trade-off of each. OUTPUT FORMAT: (1) 18-month cash table; (2) Burn-multiple calc and verdict; (3) Milestone-to-runway alignment check; (4) Two scenarios with trade-offs. CONSTRAINTS: Always include a runway buffer (assume the next raise takes longer than hoped). Show the arithmetic for burn and ending cash each month. If the plan is default-dead before the milestone, say so bluntly and prioritize the fix.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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