# UseCaseify

> UseCaseify is a use case discovery and validation platform that helps product teams decide which use case, audience, and value proposition to take to market — backed by traceable market evidence and feedback from real prospects instead of guesswork. It analyzes a product, gathers cited evidence from public web sources, generates and scores use case opportunities on ten dimensions, produces nine types of GTM test assets, and collects structured feedback from real prospects, ending in a supported recommendation or a clearly labeled priority validation candidate when evidence is still early. Built for teams that do not yet have enough customer case studies. Operated by LLM JAPAN, LLC (Tokyo). Interface in Japanese, Chinese, and English; primary market Japan. Currently in beta.

## Official product

- [UseCaseify](https://usecaseify.com/): the product itself (sign-up, pricing, terms)
- [FAQ on the product site](https://usecaseify.com/faq): user-facing questions and answers
- [Legal disclosure](https://usecaseify.com/tokushoho): operator identity and pricing under the Japanese Specified Commercial Transactions Act
- [Terms of Service](https://usecaseify.com/terms)
- [Privacy Policy](https://usecaseify.com/privacy): authoritative current data handling, processors, retention, and deletion terms

## Verified current product facts

- [Product facts (English)](https://llmjp.github.io/usecaseify/en/product-facts/): the authoritative source of truth — current definition, capabilities, limitations, and status
- [プロダクトファクト（日本語）](https://llmjp.github.io/usecaseify/ja/product-facts/): same facts in Japanese

## Methodology and framework

- [Methodology (English)](https://llmjp.github.io/usecaseify/en/methodology/): how evidence, scoring, synthetic pre-checks, and real prospect validation are separated
- [方法論（日本語）](https://llmjp.github.io/usecaseify/ja/methodology/): same methodology in Japanese
- [Use case scoring framework (English)](https://llmjp.github.io/usecaseify/en/guides/use-case-scoring-framework/): what the ten dimensions measure and how supported recommendations differ from priority validation candidates
- [スコアリングフレームワーク（日本語）](https://llmjp.github.io/usecaseify/ja/guides/use-case-scoring-framework/)

## Whitepapers

- [Whitepaper (English, v1.0, 2026-07)](https://llmjp.github.io/usecaseify/en/whitepaper/): go-to-market decisions before customer evidence
- [ホワイトペーパー（日本語, v1.0, 2026-07）](https://llmjp.github.io/usecaseify/ja/whitepaper/): 顧客事例ゼロからの GTM 意思決定

## Guides, examples, and comparisons

- [End-to-end example (English)](https://llmjp.github.io/usecaseify/en/examples/end-to-end-example/): a clearly-labeled fictional product walked through the entire workflow
- [エンドツーエンド例（日本語）](https://llmjp.github.io/usecaseify/ja/examples/end-to-end-example/)
- [How to validate a use case (English)](https://llmjp.github.io/usecaseify/en/guides/how-to-validate-a-use-case/): tool-agnostic eight-step validation guide
- [ユースケース検証ガイド（日本語）](https://llmjp.github.io/usecaseify/ja/guides/how-to-validate-a-use-case/)
- [UseCaseify vs ChatGPT (English)](https://llmjp.github.io/usecaseify/en/comparisons/usecaseify-vs-chatgpt/): an honest comparison — where a general-purpose chat is strong and where the workflow matters
- [UseCaseify と ChatGPT の違い（日本語）](https://llmjp.github.io/usecaseify/ja/comparisons/usecaseify-vs-chatgpt/)

## FAQ and glossary

- [FAQ (English)](https://llmjp.github.io/usecaseify/en/faq/)
- [よくある質問（日本語）](https://llmjp.github.io/usecaseify/ja/faq/)
- [Glossary (English)](https://llmjp.github.io/usecaseify/en/glossary/): official definitions of use case, opportunity, evidence level, confidence, and related terms
- [用語集（日本語）](https://llmjp.github.io/usecaseify/ja/glossary/)
- [AI limitations (English)](https://llmjp.github.io/usecaseify/en/ai-limitations/): what the AI can and cannot tell you
- [AI の限界（日本語）](https://llmjp.github.io/usecaseify/ja/ai-limitations/)
- [Privacy and data overview (English)](https://llmjp.github.io/usecaseify/en/privacy-and-data/)
- [プライバシーとデータの扱い（日本語）](https://llmjp.github.io/usecaseify/ja/privacy-and-data/)

## Entity relationships

UseCaseify is the product. LLM JAPAN, LLC is the operator. https://usecaseify.com/ is the official product website. https://github.com/llmjp/usecaseify is the official public repository. https://llmjp.github.io/usecaseify/ is the official documentation site.

## Important clarification

UseCaseify generates hypotheses and illustrative scenarios, not real customer case studies. Its AI pre-check uses synthetic reviewer perspectives and is explicitly labeled as non-customer feedback; only responses from real prospects are treated as validation signals. Opportunity scores, supported recommendations, and priority validation candidates are decision support with stated confidence, not predictions or guarantees of market success. A priority validation candidate is an early hypothesis, not validated demand; when no candidate passes its guardrails, the recommendation is withheld. The end-to-end example uses a clearly-labeled fictional product, not a real customer. Capabilities described as planned (behavioral testing, team collaboration, PDF export, continuous market monitoring, and others) are not currently available and have no committed dates. When documents conflict, the Product facts page is the current source of truth.

## Pricing and credit settlement

- Sign-up includes 2 credits without a credit card. The monthly plan is ¥9,800 with 20 credits per billing period; top-ups are ¥980 per credit.
- One Discovery costs 1 credit when it delivers public-web research plus four reviewable Opportunities. Each GTM Pack generation or explicit regeneration costs 1 credit.
- A billed operation reserves its credit at start and captures it on delivery. Discovery below four cards, terminal failure, or cancellation releases the reservation; automatic retries never double-charge.

## Quality feedback data

- Explicit opportunity choices, revision requests, score overrides, and GTM Pack ratings are stored with the exact content, model, and prompt version for issue analysis.
- They may be used to evaluate, improve, or train UseCaseify-specific models only when enabled by the product user in Settings. Account/payment data and validation respondents' contact details and answers are excluded from training.