WorldClone Lab
Clone the World. Test the Future.

Clone your market,experiment first

Test copy, pricing, and targeting on an AI customer panel cloned from the Korean market — before you launch. Every result tells you how far to trust it, with the evidence behind it.

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

Clone. Experiment. Decide.

Clone the market with Worlds, experiment in Console, then get evidence-backed conclusions in Signals.

→

Worlds

Clone

Shape a market-matched AI panel in minutes.

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

Console

Experiment

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

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

Signals

Decide

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.

15
0.0-0.2
15
25
0.2-0.4
25
30
0.4-0.6
30
20
0.6-0.8
20
10
0.8-1.0
10
Average (0.0)Extreme (1.0)
Average Distance (Average Distance)
A score for how different each persona is from an "average person". Closer to 0 means typical; closer to 1 means a unique combination. The wider this distribution, the more diverse the voices in your world.
Why First

Why teams experiment first

Experiment 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.

Measured
0.727
Winner Recovery
11 contrast groups · 3 samples
0.929
Control-Sign Accuracy
persona panel
0 groups
Variance Collapse
failure-mode QA
8
Public Datasets
all reproducible
See the full evidence
Teams

Use Cases

PM, Growth, Research, and Brand teams run the same experiment 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

K-Beauty Early Adopters World
Personas500
101,000
Target diversity (Diversity Score)58.0%
Average (0.0)Very diverse (1.0)
Age
25-29
Gender
Female
Job
Marketer
Lifestyle
Yoga 3x/week
Income
₩45M/year
Media
Instagram user
WORLD_001

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
New product price sensitivity test
K-Beauty Early Adopters World500 personas
Q1

How much would you pay for this product?

₩29,000₩39,000₩49,000₩59,000+
Q2

What matters most to you at this price point?

IngredientsBrandEffectPackaging
Progress0%

Processed

40

Panel agreement

78%

Execution stream & summary

LIVE PIPELINE

Processed

40

Time left

~45 sec

Responses

32

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.

directional only

Illustrative demo. In the product every result carries a confidence grade, and close calls are auto-withheld.

How grading works
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%

Relative purchase-intent signal across variants

Suggested price direction

₩49,000

42% of respondents chose this

Price band where preference held across repeated samples

Opinion Clustering (AI Segmentation)

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

Chart
3
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 the 30s working-women segment whose preference held most stably across repeats

directional only

Illustrative demo. In the product every result carries a confidence grade, and close calls are auto-withheld.

How grading works
Pricing

Pricing

Agent-side MCP execution stays free. Upgrade when you need more saved worlds, scenarios, and larger persona samples.

Free

For founders testing early ideas and first hypotheses

$0/mo
  • 3 active worlds
  • 10 saved scenarios
  • 250 personas per world
  • Free MCP agent-side runs
Start Free
Recommended

Pro

For startups running repeated validation loops

$39/mo
  • 25 active worlds
  • 250 saved scenarios
  • 2,500 personas per world
  • Advanced Signals and reports
  • Manual activation after bank-transfer confirmation
Email for transfer

Enterprise

For teams needing security, large samples, and internal data

Custom
  • Negotiated world and scenario limits
  • Custom corpus and internal data integration
  • SSO & Advanced Security
  • Dedicated validation workflow design
  • Bank transfer or contract-based billing
Contact sales

Until automated checkout is live, email contact@worldclonelab.com for Pro or Enterprise bank-transfer activation.

Public Decision MCP

Try it from your agent—no account or service key

Structure copy, pricing, ICP, and offer decisions from Claude Code or Codex, then carry them into a real test.

No WorldClone usage fee

No account, access token, or service key. Your existing agent subscription and provider usage rules still apply.

One decision at a time

Go from a guided recipe to a trust-graded Decision Memo and a real pilot draft.

Share outcomes with limits attached

Real outcomes stay labeled self-reported, and community cards are reviewable before upload.

terminal
$ claude mcp add --scope user worldclonelab -- npx -y @worldclonelab/decision-mcp

> worldclone_list_recipes
✓ copy · pricing · segment · offer
✓ channel · positioning · share
✓ no WorldCloneLab token required
1

Have Node.js and an authenticated Claude Code or Codex CLI

2

Connect the public Decision MCP with one command

3

Choose a recipe → create a memo → record the real outcome

Install the free MCPBrowse decision examples
Early Access

Start Now

Run more experiments before launch. Miss less in market.

Contact Us

Data Ethics

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