Prepare a data room and SaaS metrics pack for fundraising
A due-diligence-ready data room index and metrics pack, with the definitions investors will check and the gaps to fix first.
The prompt
Help me prepare a data room for my SaaS fundraise.
Round and stage: [e.g. Series A, $3M ARR]
Investor type: [VC / growth equity / strategic]
What we already have: [financials, cap table, contracts, metrics dashboards, board decks]
Systems of record: [billing, CRM, accounting, analytics]
Known weak spots: [e.g. messy revenue data, unsigned IP assignments, a large customer concentration]
Return:
1. A data room index with folders and documents: corporate and cap table, financials, SaaS metrics, customers and contracts, product and technology, security and compliance, team and HR, IP, legal and disputes.
2. A metrics pack spec: ARR and MRR by month, ARR bridge (new, expansion, contraction, churn), gross and net revenue retention, logo churn, cohort retention curves, CAC payback, burn multiple, gross margin, top customer concentration, pipeline. For each: the definition to state, the source system, and a common mistake to avoid (e.g. counting one-time fees or signed-not-live contracts as ARR).
3. A consistency check: which numbers must reconcile across the deck, model, and data room.
4. A prioritized fix list for my weak spots, with how to disclose anything I can't fix.
5. A list of questions investors will likely ask in diligence, with what evidence to have ready.
6. Access and confidentiality tips: staged access, watermarking, redacting customer names.
Do not invent figures; leave [ADD] placeholders. Have a lawyer review legal documents and disclosures.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Business Strategy & Startups prompts
What to fill in
- e.g. Series A, $3M ARR
- VC / growth equity / strategic
- billing, CRM, accounting, analytics
- ADD
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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