Valuing Pre-Revenue Startups with AI-Built Models

Tested prompts for pre revenue startup valuation model compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 9/10

If you're searching for a pre-revenue startup valuation model, you're probably staring at a cap table conversation, a SAFE negotiation, or a pitch deck appendix and wondering how to defend a number when you have zero revenue to point at. Traditional DCF models break without cash flows. Comparable-company multiples fall apart when your 'comps' are at Series C and you're pre-product. You need a method that works with the signals you actually have: team, market, traction proxies, and stage.

This page walks through how to use AI to build four accepted pre-revenue valuation methods side by side: Berkus, Scorecard, Risk Factor Summation, and Venture Capital Method. You feed one prompt with your startup's facts, and the model produces four triangulated valuations plus a blended range you can actually bring into a negotiation.

Below you'll find the tested prompt, four real outputs, a comparison table, and editorial guidance on when each method fits, where AI helps versus where it misleads, and how founders and early investors are using this workflow in 2025.

When to use this

Use an AI-built pre-revenue valuation model when you need a defensible number fast and your startup has no meaningful revenue yet. It's strongest when you can describe team quality, market size, product stage, and competitive context in concrete terms. The AI triangulates across established methods so you walk in with a range, not a guess.

Preparing for a pre-seed or seed raise and setting a SAFE cap
Responding to an angel investor who asked 'how did you get to that number?'
Building a board-ready appendix that shows multiple valuation methods
Stress-testing a term sheet before you sign
Advising a founder as an accelerator mentor or fractional CFO

When this format breaks down

You have meaningful recurring revenue or a clear unit economics picture. Use revenue multiples or DCF instead.
You're valuing a post-Series A company with real cash flows and burn data.
The round is already priced by a lead investor and you just need to model dilution, not valuation.
You're in a regulated transaction (409A, M&A fairness opinion) where a licensed appraiser is required.

The prompt we tested

You are a venture capital analyst specializing in early-stage startup valuation. Build a comprehensive pre-revenue valuation model for the startup described below, applying multiple accepted methodologies (Berkus Method, Scorecard Method, Risk Factor Summation, Venture Capital Method, and First Chicago Method where applicable). For each method, show the inputs, assumptions, calculations, and resulting valuation. Then synthesize the results into a recommended valuation range with reasoning.

Follow these formatting and style rules:
Structure the output with clear H2 headings for each valuation method, use markdown tables to display inputs and calculations, show all math explicitly, and conclude with a 'Recommended Valuation Range' section that gives a low/mid/high figure and a 3-5 sentence rationale. Keep the full response under 1,200 words and flag any assumptions that materially affect the outcome.

Startup details:
Our startup, LoopCharge, is building a network of modular battery-swap kiosks for electric scooters in Southeast Asian cities. We have a working prototype, 2 signed LOIs with fleet operators, a 4-person team (ex-Grab and ex-Tesla), and are raising a $1.5M seed round. No revenue yet, but we project $4M ARR by year 3 in a TAM of roughly $8B.

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	9	$0.1604	31.9s	451 / 2048
Claude Haiku 4.5	9	$0.0105	20.5s	307 / 2048
GPT-5.4	Not run yet
Gemini 2.5 Pro	Not run yet
Grok 4.1 Fast Reasoning	8	$0.0008	22.2s	416 / 1399

Outputs

Claude Opus 4.7 9/10

# LoopCharge Pre-Revenue Valuation Model

Base assumptions used across methods: Southeast Asia early-stage seed benchmark pre-money ≈ $5.0M (regional 2024 median for hardware/cleantech seed); US/global benchmark ≈ $7.0M. LOIs are treated as validation signals, not contracts. Hardware capex risk is material.

## Berkus Method

Values up to $500K per qualitative element (capped ~$2.5M pre-revenue valuation ceiling; acceptable for hardware we extend ceiling to $3M given capital intensity).

| Element | Max | LoopCharge Score | Rationale |
|---|---|---|---|
| Sound Idea (basic value) | $500K | $500K | Clear pain point; battery swap proven in China/India |
| Prototype (tech risk) | $500K | $450K | Working prototype, not yet field-tested at scale |
| Quality Mgmt Team | $500K | $500K | Ex-Grab (SEA ops) + ex-Tesla (battery) = strong |
| Strategic Relationships | $500K | $350K | 2 LOIs — directional, non-binding |
| Product Rollout / Sales | $500K | $100K | No revenue, no live deployment |
| **Total** | | **$1.90M** | |

**Berkus pre-money ≈ $1.9M** (conservative; penalizes hardware pre-deployment).

## Scorecard Method (Bill Payne)

Benchmark SEA seed pre-money: **$5.0M**.

| Factor | Weight | LoopCharge vs. Avg | Factor Score |
|---|---|---|---|
| Strength of Mgmt Team | 30% | 140% | 0.420 |
| Size of Opportunity | 25% | 150% (TAM $8B) | 0.375 |
| Product/Technology | 15% | 110% | 0.165 |
| Competitive Environment | 10% | 90% (Gogoro, local) | 0.090 |
| Marketing/Sales/Partnerships | 10% | 115% (2 LOIs) | 0.115 |
| Need for Add'l Investment | 5% | 70% (capex heavy) | 0.035 |
| Other (regulatory, etc.) | 5% | 100% | 0.050 |
| **Multiplier** | | | **1.250** |

**Scorecard pre-money = $5.0M × 1.25 = $6.25M**

## Risk Factor Summation

Start at SEA benchmark $5.0M. Each factor: +$500K (very low risk) to –$500K (very high risk) in $250K steps.

| Risk Factor | Assessment | Adjustment |
|---|---|---|
| Management | Low | +$500K |
| Stage of business | High (pre-revenue, pre-deployment) | –$250K |
| Legislation/Political | High (SEA multi-jurisdiction, EV regs) | –$250K |
| Manufacturing | High (hardware, supply chain) | –$500K |
| Sales & marketing | Normal | $0 |
| Funding/capital raising | High (capex-heavy needs Series A) | –$250K |
| Competition | Normal (Gogoro entering SEA) | $0 |
| Technology | Low | +$250K |
| Litigation | Low | +$250K |
| International | Normal (native to market) | $0 |
| Reputation | Normal | $0 |
| Exit potential | Low risk (strategic acquirers exist) | +$500K |
| **Net adjustment** | | **+$250K** |

**RFS pre-money = $5.0M + $0.25M = $5.25M**

## Venture Capital Method

Inputs:
- Year 3 projected ARR: $4M
- Exit multiple: 5x ARR (cleantech/hardware SaaS-hybrid comp)
- Projected exit value (Yr 5–6): ARR grows to ~$15M by exit; apply 5x → **$75M exit**
- Target IRR: 50% over 5 years → multiple = 1.5^5 ≈ **7.59x**
- Investment: $1.5M
- Expected dilution from future rounds: 40%

Calculations:
- Post-money at exit needed = $1.5M × 7.59 = **$11.4M** (investor's required share value at exit)
- Required ownership at exit = $11.4M / $75M = **15.2%**
- Required ownership today (accounting for 40% future dilution) = 15.2% / (1 – 0.40) = **25.3%**
- Post-money today = $1.5M / 0.253 = **$5.93M**
- **VC Method pre-money = $5.93M – $1.5M ≈ $4.43M**

| Variable | Value |
|---|---|
| Exit value | $75M |
| Target IRR / multiple | 50% / 7.59x |
| Required exit ownership | 15.2% |
| Dilution-adjusted today | 25.3% |
| Post-money | $5.93M |
| **Pre-money** | **$4.43M** |

⚠️ Materially sensitive to exit multiple and ARR ramp; a 7x exit multiple → pre-money ≈ $7.8M.

## First Chicago Method

Probability-weighted across three scenarios. Exit in Year 5.

| Scenario | Prob | Yr 5 ARR | Exit Multiple | Exit Value | Investor Share (25.3%) | PV @ 50% disc |
|---|---|---|---|---|---|---|
| Success | 25% | $25M | 6x | $150M | $37.9M | $4.99M |
| Sideways | 50% | $8M | 4x | $32M | $8.1M | $1.07M |
| Failure | 25% | $0 | — | $0 | $0 | $0 |

Probability-weighted PV of investor stake:
= 0.25 × $4.99M + 0.50 × $1.07M + 0.25 × $0
= $1.25M + $0.535M + $0
= **$1.78M**

Implied post-money = $1

Claude Haiku 4.5 9/10

# LoopCharge Pre-Revenue Valuation Analysis

## Executive Summary
LoopCharge presents a compelling early-stage opportunity in the high-growth Southeast Asian micromobility market. This analysis applies five valuation methodologies to triangulate a defensible seed-stage valuation for a $1.5M raise. **Material assumptions:** 3-year revenue projections, market adoption rates, and team execution capability significantly influence all outputs.

---

## 1. Berkus Method

The Berkus Method awards point values across five dimensions, each valued at $500K for a pre-revenue company.

| Dimension | Assessment | Points | Rationale |
|-----------|-----------|--------|-----------|
| Sound Idea | Battery-swap for e-scooters | 1.0 | Clear problem, large TAM, timing aligned with fleet operator pain |
| Prototype | Working modular kiosk | 0.9 | Functional prototype de-risks core tech; modularity untested at scale |
| Quality Team | Ex-Grab, ex-Tesla; 4 people | 0.85 | Strong backgrounds; team size small for execution scope |
| Strategic Partnerships | 2 signed LOIs | 0.75 | LOIs validate demand but non-binding; no revenue contracts |
| Other (IP/Advisors) | Pending patents; advisor TBD | 0.6 | IP protection in progress; no marquee advisors named |

**Calculation:**  
(1.0 + 0.9 + 0.85 + 0.75 + 0.6) × $500K = 4.1 × $500K = **$2,050,000**

---

## 2. Scorecard Method

Compares LoopCharge to typical seed-stage startups in the hardware/cleantech space using industry benchmarks.

| Factor | Weighting | LoopCharge Score | Benchmark Score | Multiple |
|--------|-----------|-----------------|-----------------|----------|
| Management | 30% | 8.5/10 | 7.0/10 | 1.21 |
| Product/Service | 20% | 8.0/10 | 7.5/10 | 1.07 |
| Market Size | 15% | 9.0/10 | 8.0/10 | 1.13 |
| Funding Requirement | 15% | 7.0/10 | 7.0/10 | 1.00 |
| Competitive Position | 10% | 7.5/10 | 7.0/10 | 1.07 |
| Channel/Partnership | 10% | 7.0/10 | 6.5/10 | 1.08 |

**Calculation:**  
Industry Average Seed Valuation (SEA hardware): $1.8M  
Composite Multiple: (0.30×1.21) + (0.20×1.07) + (0.15×1.13) + (0.15×1.00) + (0.10×1.07) + (0.10×1.08)  
= 0.363 + 0.214 + 0.170 + 0.150 + 0.107 + 0.108 = **1.112**  
LoopCharge Valuation: $1.8M × 1.112 = **$2,001,600**

---

## 3. Risk Factor Summation Method

Starts with industry average ($1.8M) and adjusts for risk factors (±).

| Risk Factor | Assessment | Adjustment |
|-------------|-----------|-----------|
| Market Risk | Proven demand (LOIs); $8B TAM | +$200K |
| Technical Risk | Working prototype; battery swap proven elsewhere | -$150K |
| Execution Risk | Small team; hardware complexity; supply chain | -$250K |
| Competitive Risk | Low barriers; Chinese players entering SEA | -$200K |
| Regulatory Risk | Emerging regulatory frameworks in SEA cities | -$150K |
| Funding Risk | Raising $1.5M (reasonable for stage) | +$50K |
| IP/Legal Risk | Patents pending; incorporation clean | +$75K |

**Calculation:**  
Base: $1,800,000  
Adjustments: +$200K - $150K - $250K - $200K - $150K + $50K + $75K = **-$425K**  
LoopCharge Valuation: $1,800,000 - $425,000 = **$1,375,000**

---

## 4. Venture Capital Method

Projects cash flows, applies exit multiple, and discounts back to present using target ROI.

| Input | Value | Assumption |
|-------|-------|-----------|
| Year 3 Revenue Projection | $4,000,000 | Company guidance |
| Exit Multiple (SaaS benchmark) | 6.0x | Hardware/ops-heavy; lower than pure SaaS |
| Exit Valuation (Year 5) | $24,000,000 | Assumes 2-year runway post-seed |
| Target VC Return | 10x | Typical early-stage VC expectation |
| Required Post-Money | $2,400,000 | Exit Val ÷ Target Return |
| Desired Ownership (post-money) | 62.5% | For $1.5M check |

**Calculation:**  
Post-Money Valuation = $1.5M ÷ 0.625 = **$2,400,000**  
*(Founders retain 37.5%; investors own 62.5% for $1.5M)*

**Verification:** $24M exit ÷ $2.4M post-money = 10x return ✓

---

## 5. First Chicago Method

Blends three scenarios (downside, base, upside) weighted by probability.

| Scenario | Probability | Year 3 ARR | Exit Multiple | Exit Value | Post-Money Valuation |
|----------|-------------|-----------|---------------|-----------|----------------------|
| **Downside** | 30% | $800K | 4.0x | $3.2M | $320K |
| **Base** | 50% | $4.0M | 6.0x | $24.0M | $2.4M |
| **Upside** | 20% | $12.0M | 8.0x | $96.0M | $9.6M |

**Calculation:**  
Expected Post-Money = (0.30 × $320K) + (0.50 × $2.4M) + (0.20 × $9.6M)  
= $96K + $1.2M + $1.92M = **$3,216,000**

**Note:** Downside scenario assumes one LOI fails and slower adoption; upside assumes rapid regional expansion.

---

## Synthesis & Recommended Valuation Range

| Method | Valuation | Notes |
|--------|-----------|-------|
| Berkus | $2.05M | Mid-range; reflects prototype + team strength |
| Scorecard | $2.00M | Aligned with Berkus; strong market/product scores |
| Risk Factor Summation | $1.38M | Most conservative; penalizes execution/competitive risk |
| VC Method | $2.40M | Assumes 10x return; market-clearing approach |
| First Chicago | $3.22M | Probability-weighted; reflects upside optionality |

### **Recommended Valuation Range: $1.8M – $2.4M (low-to-mid)**

**Rationale:**

LoopCharge merits a **$2.0M post-money valuation** as the midpoint for this $1.5M seed round (giving investors ~75% ownership). This reflects (1) validated demand via two LOIs and a credible team with relevant domain experience, (2) de-risked core technology via working prototype, and (3) a massive TAM ($8B) in a fast-growing region. The valuation anchors to the Berkus and Scorecard methods (which yield ~$2.0M) while remaining below the probability-weighted First Chicago scenario, which may overstate upside given execution and supply-chain risks inherent in hardware. The VC Method's $2.4M ceiling is justified only if management de-risks the team size or secures additional anchor customers pre-close. The Risk Factor Summation floor of $1.38M is defensible but likely leaves

Grok 4.1 Fast Reasoning 8/10

## Berkus Method
Assigns up to $500k per factor for pre-revenue startups, max $2.5M.

| Factor                  | Assessment                  | Value   |
|-------------------------|-----------------------------|---------|
| Sound Idea              | EV battery swap in SEA      | $500k  |
| Prototype               | Working prototype           | $500k  |
| Quality Management Team | Ex-Grab/Tesla, 4-person     | $500k  |
| Strategic Relationships | 2 LOIs with fleet operators | $500k  |
| Product Rollout Plan    | Projections but pre-sales   | $250k  |
| **Total Valuation**     |                             | **$2.75M** |

## Scorecard Method
Starts with average regional seed pre-money ($2.5M for SEA pre-revenue EV tech; *flagged assumption: based on comparable Grab/GoJek ecosystem data*). Multiplies by weighted factors (average 100%).

| Factor             | Weight | Score | Adjusted |
|--------------------|--------|-------|----------|
| Team               | 30%    | 130%  | 0.30 × 1.30 = 0.39 |
| Market/Opportunity | 25%    | 120%  | 0.25 × 1.20 = 0.30 |
| Product/Tech       | 15%    | 110%  | 0.15 × 1.10 = 0.165|
| Competitive Edge   | 10%    | 100%  | 0.10 × 1.00 = 0.10 |
| SPV/Other          | 5%     | 100%  | 0.05 × 1.00 = 0.05 |
| Sales Channels     | 10%    | 110%  | 0.10 × 1.10 = 0.11 |
| Need for Financing | 5%     | 100%  | 0.05 × 1.00 = 0.05 |
| **Total Multiplier** |      | **1.16** |         |
| **Valuation**      | $2.5M × 1.16 = **$2.9M** |         |         |

## Risk Factor Summation Method
Base: $3M (SEA seed average; *flagged*). ±$250k per 12 risks.

| Risk                  | Adjustment |
|-----------------------|------------|
| Management            | +$250k    |
| Stage of Business     | $0        |
| Legislation/Politics  | +$100k    |
| Manufacturing Risk    | -$250k    |
| Sales/Marketing       | +$250k    |
| Funding/Capital       | $0        |
| Competition           | -$250k    |
| Technology            | +$250k    |
| Litigation            | $0        |
| International          | +$100k    |
| Reputation            | +$250k    |
| Potential ROI         | +$250k    |
| **Net Adjustment**    | **+$1.0M** |
| **Valuation**         | $3M + $1M = **$4.0M** |

## Venture Capital Method
*Assumptions (flagged: material)*: $4M ARR Year 3 → $20M Year 5 (100% CAGR). Exit Year 5 at 10x ARR = $200M. Seed ROI target: 50x ($100M required). Discount rate: 60%/yr (high-risk pre-rev).

- Post-money needed: $100M / 50x = $2M (no: VC targets ownership for required return).
- Required ownership: $100M / $200M exit = 50%.
- Raising $1.5M → Pre-money = ($1.5M / 0.50) - $1.5M = **$1.5M**? Wait, correct formula:
  - Post-money V = Raise / Target Ownership = $1.5M / 0.50 = $3M.
  - Pre-money = $3M - $1.5M = **$1.5M** (conservative; *alt 40x ROI → 40% own → pre-money $2.25M*).

Adjusted for optimism: **$2.5M** pre-money.

## First Chicago Method
Scenario-weighted DCF to Year 5 exit (*flagged: growth 80-120% CAGR, discount 60%, exit 8-12x ARR*).

| Scenario | Prob. | Year 3 ARR | Year 5 ARR | Exit Value | PV of Exit | Weighted PV |
|----------|--------|-------------|-------------|------------|------------|-------------|
| Best     | 20%    | $8M        | $50M       | $600M (12x)| $600M / (1.6)^5 = $62M | 0.20 × $62M = $12.4M |
| Base     | 50%    | $4M        | $20M       | $200M (10x)| $200M / 2.98 = $67M? (1.6^5=10.48) $200M/10.48=$19.1M | 0.50 × $19.1M = $9.55M |
| Worst    | 30%    | $1M        | $2M        | $16M (8x) | $16M/10.48=$1.5M     | 0.30 × $1.5M = $0.45M |
| **Total PV** |      |             |             |            | **$22.4M** |             |
| **Pre-money (50% own target)** | $22.4M / 2? No: for $1.5M raise at 25-30% dil → **$5-6M** adjusted |             | **$5.5M** |            |             |

## Recommended Valuation Range
**$2.5M - $4.0M - $5.5M** (low/mid/high pre-money). Berkus/VC provide conservative floors due to pre-rev risks, while Scorecard/Risk/First Chicago uplift for team, LOIs, and $8B TAM. Midpoint $3.5M balances prototype traction against execution risks in SEA logistics/EV; supports $1.5M raise at ~30% dilution. Range assumes stated projections hold; downside if LOIs lapse.

*(Word count: 842)*

What makes these work

01

Feed it regional comps
Berkus and Scorecard assume US averages. If you're raising in London, Singapore, or Lagos, tell the AI the regional median pre-money and it will recalibrate. Without that input it will overstate or understate by 30-50%.
02

Describe risk in specifics
Don't say 'moderate execution risk.' Say 'hardware BOM unproven at scale, two contract manufacturers identified, no signed MOU.' Specific risk language produces defensible Risk Factor Summation scores.
03

Anchor VC Method with real exits
Give the AI 2-3 recent comparable exits with dates and multiples. The VC Method is only as good as its terminal value assumption, and generic industry averages produce generic outputs.
04

Ask for a weighted blend
The four methods will disagree. Prompt the AI to weight each method based on your stage and category, then output a single range. A range of $3M-$4.5M beats four conflicting point estimates in a negotiation.

More example scenarios

#01 · B2B SaaS at MVP stage raising pre-seed

Input

Two-person team, ex-Stripe and ex-Datadog. B2B SaaS for RevOps automation. MVP live with 8 design partners, no paid contracts yet. TAM $12B. Raising $1.5M pre-seed in US market. Competitive landscape moderate, two funded competitors at Series A.

Expected output

Berkus: $2.5M (strong team +$750k, prototype +$500k, quality board +$500k, strategic relationships +$750k). Scorecard: $4.2M (1.4x regional average, team and product lead). Risk Factor: $3.8M. VC Method: $5M post-money at 25% ownership target. Blended range: $3.5M-$4.5M pre-money.

#02 · Consumer hardware preparing angel round

Input

Solo technical founder, former Apple. Consumer sleep-tracking wearable, working prototype with 200 beta users, no sales. Hardware manufacturing risk high. TAM $4B in connected health. Raising $800k angel round. Two direct competitors acquired recently for $80M-$150M.

Expected output

Berkus: $1.75M (prototype and team strong, execution risk high). Scorecard: $2.2M (below regional average due to solo founder and hardware risk). Risk Factor: $1.9M (manufacturing and capital intensity drag). VC Method: $3M post given comp exits. Blended range: $2M-$2.5M pre-money.

#03 · AI developer tool with strong traction signals

Input

Three technical co-founders from Meta AI. Open-source dev tool with 14k GitHub stars in 4 months, no monetization yet. Enterprise pilots in discussion. Raising $4M seed. AI infra category hot, recent comps raising at $20M-$40M post.

Expected output

Berkus: $2.5M (capped by method). Scorecard: $12M (2.4x average, top-quartile team and momentum). Risk Factor: $10M. VC Method: $16M post-money. Blended range: $10M-$14M pre-money, with Scorecard and VC Method carrying more weight given market signal.

#04 · Biotech pre-clinical seeking seed extension

Input

Scientific founder with two prior exits in oncology. Pre-clinical stage therapeutic, IP filed, no animal data yet. TAM large but regulatory path 7+ years. Raising $3M seed extension. Dilution-sensitive given future capital needs.

Expected output

Berkus: $2.5M. Scorecard: $6M (team premium, offset by technology risk). Risk Factor: $4.5M (regulatory and capital risks heavy). VC Method: $8M post assuming $500M exit in 8 years at 30x return target. Blended range: $5M-$7M pre-money.

#05 · Marketplace pre-launch in emerging market

Input

Co-founders in Lagos, fintech marketplace connecting SMB lenders and borrowers. Closed beta, 50 loans facilitated, no revenue yet. Raising $500k pre-seed. Local comps limited, regional valuations typically 40-60% of US equivalents.

Expected output

Berkus: $1.5M (regional adjustment). Scorecard: $2M (using African regional average of $2.5M). Risk Factor: $1.8M. VC Method: $2.5M post. Blended range: $1.5M-$2M pre-money. Flagged: US-centric methods may undervalue local market knowledge.

Common mistakes to avoid

Treating AI output as the answer
These models produce directional ranges, not appraisals. If you walk into a raise quoting '$4.2M pre-money because ChatGPT said so,' you will lose the room. Use it to prepare, not to decide.
Skipping the market size input
Without a credible TAM figure, Scorecard defaults to mediocre multipliers and the VC Method terminal value is meaningless. A vague 'large market' input produces a vague valuation.
Using Berkus past seed stage
Berkus caps at around $2.5M by design. If you're raising a $10M seed for an AI company, Berkus will anchor you low. Drop it from the blend or flag it as a floor only.
Ignoring dilution math
A $5M pre-money with a $2M raise means 28.5% dilution. Many founders accept the valuation number without modeling what the cap table looks like after. Always pair valuation output with a dilution scenario.
Pasting the output into a deck verbatim
Investors recognize AI-generated valuation language. Rewrite the logic in your own voice and cite the methods explicitly. 'We triangulated Berkus, Scorecard, and VC Method' lands better than a copy-paste table.

Related queries

Frequently asked questions

What is the best valuation method for a pre-revenue startup?

There isn't a single best method. Most serious investors triangulate across Berkus, Scorecard, Risk Factor Summation, and the VC Method. Each captures different signal: Berkus rewards tangible progress, Scorecard benchmarks against regional peers, Risk Factor prices execution risk, and VC Method works backward from an exit. Use all four and present a range.

How do I value a startup with no revenue and no users?

Lean on Berkus and Risk Factor Summation. Berkus gives credit for team, idea quality, prototype, strategic relationships, and early product rollout, even without traction. Expect a range of $1M-$2.5M pre-money for most geographies. If the team has prior exits or deep domain expertise, Scorecard can push higher.

Can AI actually produce a defensible startup valuation?

AI can produce a defensible range quickly, but the defense comes from the method, not the tool. If you can explain why Scorecard gave you $4M and why you weighted it 40% in the blend, that's defensible. If you can only say 'the AI output was $4M,' it's not.

What's a realistic pre-seed valuation in 2025?

US pre-seed pre-money valuations typically range $3M-$8M, with AI and dev tools trending higher ($8M-$15M) and consumer or hardware trending lower ($2M-$5M). Europe and emerging markets usually run 30-50% below US comps. Always check recent deal data in your specific category.

How does the VC Method work for pre-revenue startups?

Estimate a realistic exit value in 5-8 years (usually based on comparable acquisitions or IPOs), divide by the return multiple the investor needs (typically 10x-30x at this stage), then adjust for future dilution. That gives you post-money today. Subtract the round size to get pre-money.

Should I use Berkus or Scorecard for my seed round?

Use both. Berkus gives a conservative floor and works best at pre-seed or idea stage. Scorecard scales with team quality and market size and tends to fit seed rounds better. If they disagree by more than 2x, something in your inputs is off, usually the regional average or team assessment.