A synthetic-consumer platform built for the Japanese market.
1,000 AI agents generated from the Japan Census validate concepts, packaging, and pricing before launch.
Correlation with real purchase data: r = 0.78–0.92. First report delivered in as little as 72 hours.
Surveys, focus groups, and Top2Box purchase-intent scores are well-honed methods that remain essential in the final validation phase. But the people who say "I'll buy it" do not always buy — a structural gap known as the Say-Do Gap. Launch Simulation is a complementary method that fills this gap by simulating behavior — not preference — in the screening and shortlisting stages.
Up to 25% of respondents who say "I'll buy it" never actually purchase. No amount of survey-design refinement removes the structural error inherent in self-report.
A single quantitative panel (N=1,000, 20–25 questions) typically runs ¥0.5–1.5M (close to ¥2M with heavy video/image or multi-SKU comparisons). Cycle time of 4–8 weeks structurally limits how many concepts you can test.
Share of new products that exit the market within two years of launch. Source: Nielsen/NIQ 2023, ESOMAR 2024. Even with thorough research, this number has not improved.
The accuracy of synthetic consumers is determined by the depth of training data and how well it fits a benchmark. Three peer-reviewed studies support the validity of this approach, and we have validated against real purchase data in the beverage category. We can reproduce your past surveys under a Blind Validation protocol and prove fit with a quantitative reconciliation report.
Large-scale validation using data from over 2,000 real consumers. AI digital twins demonstrated high accuracy in economic and social domains such as purchase decisions and price sensitivity.
Compared against 57 real consumer studies (9,300 respondents), AI synthetic consumers achieved 90% of human test-retest reliability. Scoring natural-language responses by semantic similarity is the key.
Equipping AI personas with detailed life context, values, and purchase habits improves alignment with real surveys by up to 87%. We embed life context derived from the Japan Census and Household Survey.
Reconciliation against real purchase data is ongoing. Through Blind Validation, your team can independently confirm reproducibility on your own data.
Census-based population generation × Population-True Approach × Three-layer judgment architecture
Hand off your existing concept materials as-is. We generate AI personas, run simulations in parallel, and reconcile the results internally — then deliver a report shaped to your team's decision format.
Product name, target audience, price range, positioning, concept board. PDF, PPT, or Word — any format. No reformatting required.
1,000 agents aligned with demographics, household spending, and category purchase history simulate behavior at the shelf — each grounded in their own context (family, budget, loyalty to existing brands).
Purchase probability, Top2Box by segment, price-sensitivity curves, main switching sources, and improvement implications. Delivered in a format ready to drop into your internal approval workflow.
Enter product details below and press "Run Simulation". The Claude API simulates 5 real Japanese consumers, evaluating each one's life context, inner voice, and price acceptability. The full version delivers the same resolution across 1,000 personas, aggregated by segment.
Not a replacement for traditional panel surveys — an upstream layer that feeds into them. We increase the density of decisions made at the screening stage, while panel surveys continue to anchor the final validation phase.
Product planning for beverages, confectionery, and household goods; agency pitch phases; the upstream layer of research firms. Core use cases: launch go/no-go, concept narrowing, pre-launch price-sensitivity testing, and renewal decisions.
Quantitative panel surveys in Japan (N=1,000, 20–25 questions) typically run ¥0.5–1.5M per concept. Launch Simulation starts at ¥150,000 per concept. Generating and running 1,000 AI personas is dominated by server cost, so unit cost does not scale linearly with volume — the more concepts you test in parallel, the lower the unit price.
Validate a single concept with 1,000 AI personas. Delivered in as little as 72 hours.
Simulate 3–10 concepts in parallel. Built for screening-stage narrowing.
For product development teams, agencies, and research firms. Annual contracts / API / OEM.
* Pricing varies with question count, category, and lead time. A firm quote is provided after NDA.
* Start with Blind Validation (free). Confirm accuracy against your past tests before moving to paid work.
* Source for traditional benchmarks: published rate cards from Research Boutique, Rakuten Insight, Macromill, and Asmarq (2024–2025).
No contract required up front. We'll learn about your situation and recommend which next step fits best — Blind Validation, Pilot Test, or Full Engagement. Confidentiality applies from the first 30 minutes; you can talk to us without sharing any sensitive information.
A senior team member will hear out your product, your challenge, and your accuracy expectations, then propose the best path forward.
Run the simulation blind to your past test results. We deliver a quantitative reconciliation report showing how closely we hit them.
Accuracy benchmarks, pricing plans, and case studies (24-page PDF). Built for internal sharing and approval workflows.