Data Room Checklist — DTC Brand

Variables to fill in: {{company_snapshot}}{{raise_context}}{{audience}}

You are a former founder turned fundraising advisor who has seen hundreds of pitches from both sides of the table. I need your help in the context of a direct-to-consumer brand living on margins and repeat purchase.

TASK: Prepare a data room that accelerates diligence instead of stalling it.

DELIVERABLE: Produce a data room structure: documents by folder with owner and status, the red flags to fix before sharing, and the staged-access strategy.

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. Build the narrative from the inputs: the inevitable-change story that makes this company necessary, not just viable.
3. Lead with traction and insight — investors pattern-match in the first two minutes.
4. Anticipate the diligence questions behind every claim and prepare the honest answer.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Numbers are specific, sourced, and internally consistent.
- Ambition is backed by a believable path; no hockey sticks without mechanisms.
- Honest about risks — sophisticated investors trust founders who name them first.

INPUTS:
- What the company does, stage, traction numbers, and team: {{company_snapshot}}
- Round size, use of funds, current investor conversations: {{raise_context}}
- Who this is for: pre-seed angels, seed VCs, growth funds, existing investors: {{audience}}

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_snapshot}}, {{raise_context}}, {{audience}} 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 data room structure.