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Early-Stage Business Idea Feasibility & Risk Pre-Mortem

Conduct a rigorous pre-mortem analysis to identify fatal assumptions, distribution bottlenecks, and market risks before writing code.

#business-validation#startup#pre-mortem#risk-analysis#market-feasibility

Compatibility & Specs

Compatible AI Models
ClaudeChatGPTGemini
Last UpdatedOct 3, 2026
Customizable Variables4 parameters
Main Prompt Template

162 words • 1204 characters

You are a seasoned venture builder and startup risk analyst known for rigorous, unsentimental feasibility reviews. I want to conduct a comprehensive Pre-Mortem and Feasibility Teardown on a new business idea. Business Concept Overview: [business_concept] Target Customer & Problem: [target_customer_problem] Proposed Monetization Model: [monetization_model] Primary Growth / Distribution Hypothesis: [distribution_channel] Analyze this business concept with the following rigorous breakdown: 1. The 18-Month Pre-Mortem: Fast-forward 18 months assuming this company shut down. Detail the top 3 most likely root causes of failure (focus on distribution failure, low willingness-to-pay, retention cliff, or unit economic leakage). 2. Critical Assumption Inventory: List the 5 make-or-break hypotheses that must be true for this business to reach $1M ARR, ranked by uncertainty and impact. 3. Defensibility & Moat Evaluation: Analyze how vulnerable this is to platform risk, fast-following incumbents, and AI commoditization. 4. Day-Zero De-risking Experiments: Propose 3 low-cost, non-technical validation experiments I can run in the next 14 days to validate customer pull before building any product.
Tip:Variables marked in purple brackets can be tailored below.

Customize Prompt

Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.

Parameters (4)* Indicates required field
[business_concept]

What the product or service does and its core mechanism

[target_customer_problem]

Who experiences the pain and what the financial cost of that pain is

[monetization_model]

How revenue is generated (SaaS subscription, marketplace take-rate, usage-based)

[distribution_channel]

Primary acquisition lever to reach customers cost-effectively

4 parameters active
Live Customized OutputLive Preview
224 words
You are a seasoned venture builder and startup risk analyst known for rigorous, unsentimental feasibility reviews. I want to conduct a comprehensive Pre-Mortem and Feasibility Teardown on a new business idea. Business Concept Overview: An automated inventory forecasting and deadstock liquidation platform for independent boutique retailers. Target Customer & Problem: Independent apparel boutiques with 1-3 physical locations who lock up 35% of cash flow in unsold seasonal garments and lack automated sell-through insights. Proposed Monetization Model: $149/month subscription fee plus a 5% commission on secondary wholesale liquidation batches. Primary Growth / Distribution Hypothesis: Integrations with Shopify POS / Square, co-marketing through regional retail trade associations, and cold email outreach to store owners. Analyze this business concept with the following rigorous breakdown: 1. The 18-Month Pre-Mortem: Fast-forward 18 months assuming this company shut down. Detail the top 3 most likely root causes of failure (focus on distribution failure, low willingness-to-pay, retention cliff, or unit economic leakage). 2. Critical Assumption Inventory: List the 5 make-or-break hypotheses that must be true for this business to reach $1M ARR, ranked by uncertainty and impact. 3. Defensibility & Moat Evaluation: Analyze how vulnerable this is to platform risk, fast-following incumbents, and AI commoditization. 4. Day-Zero De-risking Experiments: Propose 3 low-cost, non-technical validation experiments I can run in the next 14 days to validate customer pull before building any product.
Launch in:
ChatGPTClaudeGemini

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

01

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

02

2. Send to AI Model

Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.

03

3. Review and Iterate

Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.

Prompt Variables & Parameters

Reference breakdown of every dynamic variable embedded in this prompt template.

PlaceholderParameter NameTypeStatusDescription & Guidance
[business_concept]Business Concept OverviewtextareaRequiredWhat the product or service does and its core mechanismDefault: An automated inventory forecasting and deadstock liquidation platform for independent boutique retailers.
[target_customer_problem]Target Customer & ProblemtextareaRequiredWho experiences the pain and what the financial cost of that pain isDefault: Independent apparel boutiques with 1-3 physical locations who lock up 35% of cash flow in unsold seasonal garments and lack automated sell-through insights.
[monetization_model]Monetization ModeltextRequiredHow revenue is generated (SaaS subscription, marketplace take-rate, usage-based)Default: $149/month subscription fee plus a 5% commission on secondary wholesale liquidation batches.
[distribution_channel]Distribution ChanneltextRequiredPrimary acquisition lever to reach customers cost-effectivelyDefault: Integrations with Shopify POS / Square, co-marketing through regional retail trade associations, and cold email outreach to store owners.

Example Execution & Benchmark Output

Sample input arguments and the verified AI response demonstrating expected quality and formatting.

Sample VariablesInputs
[business_concept]
An automated inventory forecasting and deadstock liquidation platform for independent boutique retailers.
[target_customer_problem]
Independent apparel boutiques with 1-3 physical locations who lock up 35% of cash flow in unsold seasonal garments and lack automated sell-through insights.
[monetization_model]
$149/month subscription fee plus a 5% commission on secondary wholesale liquidation batches.
[distribution_channel]
Integrations with Shopify POS / Square, co-marketing through regional retail trade associations, and cold email outreach to store owners.
Expected AI ResponseVerified Result
### 1. 18-Month Pre-Mortem: Why This Venture Failed - **Liquidation Marketplace Imbalance (The Liquidity Trap):** The SaaS forecasting tool worked, but boutique owners demanded instant liquidation. The buyer side (off-price discount buyers) demanded deep 80% markdowns that boutique owners refused to take, resulting in a stagnant secondary marketplace and churn. - **High Support Burden vs. Low ACV:** At $149/month ($1,788 ACV), store owners expected high-touch onboarding and custom POS mapping. Customer Success costs exceeded Year 1 gross profit margin, rendering unit economics unsustainable. - **POS Ecosystem Dependency:** Major POS platforms (Square, Shopify) rolled out native predictive replenishment widgets into their core dashboard, cutting off top-of-funnel acquisition. ### 2. Critical Assumption Inventory 1. **Willingness to Liquidate:** Boutique owners value recovering cash over brand dilution when clearing inventory. 2. **POS Data Hygiene:** Small boutiques maintain accurate SKU barcodes and inventory counts (often false in practice). 3. **Channel CAC:** Independent retail owners can be acquired for under $400 blended CAC via cold channels. ### 3. Day-Zero De-risking Experiments - **The Manual Spreadsheet Audit:** Offer 10 local boutique owners a free 'Deadstock Audit' using their exported CSVs. Manually find $5,000 of locked capital. If they won't pay $100 for you to broker the liquidation manually, software won't fix it. - **Buyer Demand Smoke Test:** Create a one-page catalog of 50 deadstock items and pitch regional discount liquidators. Verify if buyer demand exists at sustainable discounts.

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Founders validating new venture ideas before investing personal capital or engineering resources
Product leaders assessing the viability of spinning out a new commercial product line
Angel investors and accelerators screening pitch concepts for unaddressed structural vulnerabilities

Tips for Best Results

Techniques to elevate response fidelity

  • •Be completely transparent about your distribution vulnerabilities; AI cannot evaluate risks you omit.
  • •Pay close attention to whether customer acquisition relies on expensive paid ads or defensible natural loops.

Common Mistakes to Avoid

Frequent failure modes and anti-patterns

  • •Confusing customer interest ('that sounds neat') with true budget authority and purchasing intent.
  • •Assuming high software margins without calculating the real cost of onboarding and data cleanup.

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