Using GPT Agents for Investment Memo Creation
Here's an example of how we used various GPT 'agents' to handle different tasks in analyzing and pitching Curen. Each row in the table below outlines a specific memo section, the associated GPT agent, example prompt, and the resulting output.
Deal Snapshot
GPT Agent Used:
Deal Structurer
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Deal Snapshot
Example Content:
- **Pre-Money Valuation:** $4M
- **Team's Investment:** $0.5M (Total $1.0M round)
- **Post-Money:** $5M
- **Ownership:** 10%
Example Prompt:
"If our team invests $0.5M in a $1.0M round at a pre-money valuation of $4M, what will the post-money valuation and our ownership percentage be?"
Agent Output:
Calculated post-money valuation as $5M, 10% equity stake from a $0.5M investment.
Top 5 Reasons for Investing
GPT Agent Used:
Market Maven, Technology Analyst
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Top 5 Reasons for Investing
Example Content:
1. Massive market need for grid storage ($10B+, ~27% growth).
2. Unique, sustainable copper-based battery technology.
3. Strong IP position (patent WO2024184590A1).
4. Proof-of-concept prototype underway (5kW/25kWh).
5. High climate impact and strong industry tailwinds.
Example Prompt:
Market Maven: "Current size & growth of grid-scale storage market?"
Technology Analyst: "Advantages of copper-based flow batteries vs. lithium-ion and vanadium?"
Agent Output:
Market Maven confirmed grid-scale market ~$10B+, 25-30% annual growth.
Technology Analyst confirmed copper's sustainability, lower cost, and reliability advantages.
Top 5 Reservations (Risks)
GPT Agent Used:
Risk Auditor
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Top 5 Reservations (Risks)
Example Content:
1. Unproven tech at scale (performance speculative).
2. High capital needs, long time to commercialization.
3. Strong market competition and adoption challenges.
4. Lack of experienced commercial team.
5. Regulatory hurdles and long utility sales cycles.
Example Prompt:
"List the top 5 risks for early-stage grid battery startups (tech, market, team, financial)."
Agent Output:
Highlighted tech scale-up risk, financial runway/capital needs, competitive landscape, team execution gap, and regulatory/market adoption risks.
Investment Decision & Rationale
GPT Agent Used:
Decision Synthesizer
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Investment Decision & Rationale
Example Content:
**Decision:** Invest in Curen
**Rationale:** High-risk/high-reward; large market pain point, unique tech, upcoming prototype milestone validation, strong alignment with climate mission.
Example Prompt:
"Draft short investment recommendation based on pros (large market, unique tech) and cons (significant risks)."
Agent Output:
Recommended emphasizing high potential rewards balanced against risks, prototype validation milestone importance, and climate impact alignment as key rationale.
Valuation Rationale
GPT Agent Used:
Valuation Guru
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Valuation Rationale
Example Content:
$4M pre-money valuation justified by market comparables ($2-5M range), high technical risks, seed-stage industry benchmarks, and targeted 10% equity stake for meaningful upside.
Example Prompt:
"Appropriate valuation for pre-revenue battery tech startup with prototype and patent pending?"
Agent Output:
Suggested pre-money valuation of $2–5M for comparable early-stage energy startups, confirming a $4M valuation as reasonable given Curen's stage, risks, and IP strengths.
Expected Return Analysis
GPT Agent Used:
Return Analyst
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Expected Return Analysis
Example Content:
Target return: ~10x investment over 5-7 years (≈58% IRR for $50M exit; 5x return ≈38% IRR at $25M exit). High asymmetric payoff: acceptable risk profile with potential substantial upside from successful technology deployment and adoption.
Example Prompt:
"Calculate returns and IRR for a $0.5M investment at $5M post-money valuation if exited at $50M or $25M in 5 years."
Agent Output:
Provided ~10x return (58% IRR) for a $50M exit and ~5x return (38% IRR) for a $25M exit, confirming the investment's attractive upside and risk-reward balance.
Due Diligence Strategy (SERAF)
GPT Agent Used:
Diligence Coach
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Due Diligence Strategy (SERAF)
Example Content:
Focused on verifying assumptions (tech feasibility, market interest, team execution, funding runway):
- Prototype scalability & performance?
- Utility or customer interest?
- Commercialization & team-building plans?
- Funding milestones & next rounds strategy?
Example Prompt:
"Suggest due diligence questions for founder of pre-revenue battery startup covering tech, market, team, financial."
Agent Output:
Produced targeted questions around tech challenges, customer validation, commercialization strategy, team-building, and financial runway needs, directly supporting SERAF-style diligence checklist.
Final Pitch Preparation
GPT Agent Used:
Pitch Polisher, Editorial Reviewer
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Final Pitch Preparation
Example Content:
- Refined investment thesis emphasizing clear market, unique tech, balanced risks.
- Edited for clarity, brevity, impactful messaging, and presentation readiness.
Example Prompt:
Pitch Polisher: "Rewrite investment thesis concisely and impactfully for pitch."
Editorial Reviewer: "Proofread and trim executive summary draft; highlight unclear or wordy sections."
Agent Output:
Pitch Polisher clarified narrative (market problem → unique solution → risks balanced by potential upside). Editorial Reviewer suggested concise language and minor edits to ensure clarity and fit page constraints.