WorldClone is a market validation engine that simulates user reactions pre- and post-launch with AI panels that mirror real-world distributions, helping teams de-risk and iterate faster.

How does the AI panel work?

We combine thousands of persona presets to generate AI panels that reflect real population distributions, then run concept, price, and message tests instantly to predict user reactions.

PMs, growth marketers, brand teams, and research teams at B2B companies who need to validate their products before launch.

Clone the World. Test the Future.

BUILD YOUR WORLD
TEST INFINITE IDEAS

Validate the hypothesis before launch. Test pricing, messaging, and onboarding with thousands of AI personas.

Before Launch

Launch is too late to learn

If the hypothesis is wrong at launch, the cost compounds.

Cost Barrier

External research is too expensive for early-stage validation loops.

Time Consuming

By the time recruiting and analysis finish, the market has already moved.

Data Reliability

Thin samples and weak response quality hide the signals that matter.

Why now

LLMs are now good enough to model believable personas. WorldClone turns that diversity into a usable validation loop.

Core Loop

Build. Simulate. Decide.

Worlds, Console, and Signals form one validation loop.

→

Worlds

Build

Shape a market-matched AI panel in minutes.

10,000+ persona presets
Demographic-matched sampling
Auto-calculated diversity scores
Edge case detection

→

Console

Simulate

Run pricing, messaging, and onboarding tests in one batch.

Batch test execution
Time-axis simulation
Segment-specific questions
Real-time results

Signals

Analyze

Turn raw responses into clusters, sentiment, and next actions.

Key objection extraction
Segment difference analysis
Feature priority ranking
Actionable suggestions

Distance from Average Distribution

Visualize how much each persona deviates from the average. This distribution shows your world's diversity.

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Average (0.0)Extreme (1.0)

Average Distance (Average Distance)

We calculate how much each persona deviates from a "typical person" using the RMS method. Values closer to 0 are more typical, while values closer to 1 indicate unique combinations. This distribution allows you to quantitatively assess cohort diversity.

Why First

Why teams simulate first

Validate faster, wider, and with less risk.

Extreme Diversity

You see the distribution, not just the average, so edge cases show up earlier.

Overwhelming Speed

You can test and revise in the same day, not weeks later.

Risk-Free Experiments

Test pricing, positioning, and bold messaging before risking the brand.

Teams

Use Cases

PM, Growth, Research, and Brand teams run the same validation loop.

Product Managers

Prioritize the roadmap and catch onboarding friction before launch.

Feature priorityOnboardingPricing testsRoadmap validation

Growth Teams

Find which value props and messages actually convert.

Message testsLanding copyPricingConversion

UX Researchers

Pressure-test hypotheses before interviews and segment studies.

Interview prepSegmentsHypothesesCompetitors

Brand Teams

Catch positioning and copy conflicts before the market does.

Copy testsPositioningPerceptionNaming

STEP 1 · WORLDS

Create the AI panel

Shape a market-matched cohort in minutes

World creation settings

World name

K-Beauty Early Adopters World

Personas500

101,000

Target diversity (Diversity Score)58.0%

Average (0.0)Very diverse (1.0)

Persona element examples

Age

25-29

Gender

Female

Job

Marketer

Lifestyle

Yoga 3x/week

Income

₩45M/year

Media

Instagram user

WORLD_001

500

K-Beauty Early Adopters World

Women 20-35, high beauty interest, active online shopping

Total

500

Diversity

72.0%

Distance

58.0%

✓ Created: 500 unique personas generated based on your parameters. You can now run simulations in Console.

STEP 2 · CONSOLE

Run Scenarios

See setup, execution, and the key signal on one screen

Scenario setup

Auto run

Scenario name

New product price sensitivity test

Target world

K-Beauty Early Adopters World500 personas

Questions

How much would you pay for this product?

₩29,000₩39,000₩49,000₩59,000+

What matters most to you at this price point?

IngredientsBrandEffectPackaging

Progress0%

Processed

Signal confidence

78%

Execution stream & summary

LIVE PIPELINE

Processed

Time left

~45 sec

Responses

Avg response time

2.7s

Loading scenario

Preparing personas

Generating responses

Aggregating & analyzing

Structuring the prompt and target world

Price-sensitivePrice resistance found

I can accept ₩49,000, but past ₩59,000 I would start comparing alternatives.

Ingredient-ledTrust signal matters

If the effect is similar, transparent ingredients and trust signals would decide the purchase.

Brand-ledPremium upside exists

I would accept a higher price if the brand tone and packaging feel premium enough.

Purchase intent

52%

Recommended price

₩39,000

Readable at a glance

WorldClone compresses price resistance, segment differences, and next action from one question set so the team can decide quickly.

STEP 3 · SIGNALS

Turn responses into signals

Compress raw feedback into clusters, intent, and next actions

Overall sentiment

62.0%Positive

Positive 62%Neutral 28%Negative 10%

Purchase Intent

68%

Share of simulation personas with positive purchase intent

Derived optimal price

₩49,000

42% of respondents chose this

Optimal point considering profitability and conversion

Opinion Clustering (AI Segmentation)

Discover hidden needs and support decisions through segment-level clustering, not simple averages.

Chart

Value-focused group

35%

Price-sensitive

Quality/ingredients group

45%

Ingredient-conscious

Brand image group

20%

Image-driven

Recommended actions

1. Price tier strategy

Keep main product at ₩49,000; launch mini (₩29,000) for 20s to lower barrier

2. Ingredients marketing

Place 'EWG Green' certification and full ingredients at top of product page

3. Focus on 30s segment

Allocate 70% of initial ad spend to 30s working women with highest PMF score

Agent-Native

Use directly from your AI agent

Connect via MCP from Claude Code, Codex, or any MCP-compatible agent. Automate market validation without writing code.

Zero API Cost

Generate persona responses + qualitative analysis using your agent's own LLM subscription. No additional charges.

Full Pipeline

World creation → scenario execution → response storage → sentiment analysis → report download — all via 13 MCP tools.

LLM Qualitative Analysis

Not keyword matching — your agent's LLM performs semantic theme clustering, segment analysis, and hidden pattern detection.

.mcp.json

{
  "mcpServers": {
    "worldclonelab": {
      "command": "node",
      "args": ["worldclonelab-mcp/dist/index.js"],
      "env": {
        "WORLDCLONELAB_ACCESS_TOKEN": "wclat_..."
      }
    }
  }
}

Settings > Access Tokens > 토큰 발급

.mcp.json에 토큰 설정

Claude Code / Codex에서 바로 사용

Get Access Token View MCP Docs

Early Access

Start Now

Run more experiments before launch. Miss less in market.

Data Ethics

We simulate population distributions, not individuals
Synthetic results are decision support tools
All outputs are labeled as synthetic data

BUILD YOUR WORLDBUILD YOUR WORLDBUILD YOUR WORLDTEST INFINITE IDEASTEST INFINITE IDEASTEST INFINITE IDEAS

Launch is too late to learn

Cost Barrier

Time Consuming

Data Reliability

Why now

Build. Simulate. Decide.

Worlds

Console

Signals

Distance from Average Distribution

Why teams simulate first

Extreme Diversity

Overwhelming Speed

Risk-Free Experiments

Use Cases

Product Managers

Growth Teams

UX Researchers

Brand Teams

Create the AI panel

World creation settings

K-Beauty Early Adopters World

Run Scenarios

Scenario setup

Execution stream & summary

Turn responses into signals

Overall sentiment

Purchase Intent

Derived optimal price

Opinion Clustering (AI Segmentation)

Recommended actions

Use directly from your AI agent

Zero API Cost

Full Pipeline

LLM Qualitative Analysis

Start Now

Data Ethics

BUILD YOUR WORLD
TEST INFINITE IDEAS