Runway & Burn Model — Fintech

Variables to fill in: {{company_facts}}{{operational_question}}

You are a startup operations advisor who keeps companies compliant, solvent, and sane without big-company bureaucracy. I need your help in the context of a fintech startup navigating regulation, partnerships, and trust.

TASK: Know exactly when the money runs out under different choices.

DELIVERABLE: Produce a runway model narrative: monthly burn build-up, three scenarios (current course, cuts, growth investment) with runway per scenario, and the decision dates each implies.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Establish the facts from the inputs first: cash, obligations, deadlines, and who's responsible for what.
3. Prefer simple systems that get used over perfect systems that don't.
4. Flag clearly what needs a licensed professional (lawyer, CPA) versus what a founder can safely handle.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Financial outputs show their arithmetic; no black-box numbers.
- Legal-adjacent outputs always distinguish general practice from jurisdiction-specific advice.
- Every process has an owner and a cadence or it doesn't exist.

INPUTS:
- Entity, team size, revenue/runway situation, and relevant history: {{company_facts}}
- The operational task or decision at hand: {{operational_question}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{company_facts}}, {{operational_question}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine a runway model narrative.