UseCaseify FAQ

What is 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 your product, gathers cited evidence from public web sources, generates and scores use case opportunities, produces GTM test assets, and collects structured feedback from real prospects. It is built for teams that do not yet have enough customer case studies.

Who is UseCaseify for?

Early-stage B2B SaaS and AI product founders before product-market fit, product marketing and GTM leads choosing value propositions for personas or industries, and agencies or consultants who need evidence-backed market-entry recommendations for clients. The interface is in Japanese, Chinese, and English; the primary market is Japan.

What does UseCaseify actually produce?

A confirmed product profile; a workspace of supporting and contradicting market evidence with sources; four reviewed use case opportunity cards, each scored on ten dimensions with a separate confidence rating; nine types of GTM test assets per opportunity; a shareable prospect validation page; and an immutable decision report with the recommendation, main uncertainties, and next tests.

How is UseCaseify different from asking ChatGPT?

A general-purpose AI chat produces plausible ideas in one pass. UseCaseify runs a structured decision workflow: real web research with verbatim, cited evidence including contradicting evidence; red-team screening of candidates; deterministic ten-dimension scoring with confidence tracked separately; claim checks on generated assets; and a path to feedback from real prospects. The goal is not more ideas but a defensible decision about which idea to test.

Are the generated use cases real customer case studies?

No. Generated use cases are hypotheses or illustrative scenarios grounded in market evidence. They are not real customer case studies, and UseCaseify labels them accordingly. They must not be published as if they described real named customers or real measured results.

Is the AI pre-check real market validation?

No. The AI pre-check applies five synthetic reviewer perspectives to stress-test an opportunity before you spend anything on outreach. Its results are explicitly labeled as synthetic, non-customer feedback. Only responses from real prospects, collected through validation pages, are treated as validation signals.

How does prospect validation work?

You publish a validation page with 1–8 questions, public or password-protected, and share the link with prospects. Respondents answer anonymously without creating an account, consent is handled explicitly, and leaving contact details is optional. Responses are summarized and clustered into insights, which produce score-change suggestions that apply only when you accept them.

How does opportunity scoring work?

Each opportunity is scored on ten dimensions: pain frequency, pain severity, urgency, existing spend, product fit, reachability, differentiation, evidence quality, testability, and strategic fit. Each dimension is reasoned independently, and the total is a deterministic weighted sum, not a model’s overall impression. Confidence (low, medium, high) and evidence level are reported separately, and you can override scores manually. A qualifying candidate becomes a supported recommendation. If none qualifies but one passes guardrails for confidence, repeated signals, grounding, product fit, testability, and red-team survival, it is labeled a priority validation candidate—an early hypothesis, not validated demand. If none passes, no recommendation is named.

Can UseCaseify guarantee that my product will succeed?

No. Scores and recommendations are decision support with stated confidence and evidence levels. UseCaseify does not predict or guarantee product-market fit, conversion rates, or any business outcome.

What data sources does UseCaseify use?

The product information you provide (URL, files, or text), public web pages found through research and quoted verbatim with sources, evidence you add manually, and responses from real prospects on validation pages. Website ingestion is limited to publicly reachable pages with a bounded crawl. UseCaseify does not use private databases or claim real-time market feeds.

How much does UseCaseify cost?

Pricing is credit-based. Sign-up includes 2 credits with no card required. One Discovery (public-web research plus four reviewable Opportunities) costs 1 credit; each GTM Pack generation or explicit regeneration costs 1 credit. Credits are reserved at start and captured on delivery; a Discovery below four cards, terminal failure, or cancellation releases the reservation, and automatic retries never double-charge. As of July 2026 the monthly plan is ¥9,800 including tax with 20 credits per billing period, and top-ups are ¥980 per credit. The authoritative pricing is on usecaseify.com and its legal disclosure page.

Can I use UseCaseify before I have any customers?

Yes — that is the situation it is built for. UseCaseify does not require existing customers, case studies, or analytics data. It starts from your product itself (URL, documents, or text), grounds candidate use cases in public market evidence, and helps you collect your first structured feedback from real prospects.

Does UseCaseify replace customer interviews?

No. Talking to customers remains the strongest evidence you can get. UseCaseify narrows what to ask by identifying which use case and which assumptions deserve testing first, and it generates an interview guide as one of its GTM assets. Validation pages complement interviews; they do not replace them.

Which parts are generated by AI, and which are not?

AI drafts the product profile, runs research and classifies evidence, generates and revises opportunity candidates, reasons dimension scores, writes GTM assets, runs the synthetic pre-check, and clusters validation responses into suggestions. Not AI: the verbatim quotes inside evidence (real source text), the deterministic score totals (arithmetic), responses from real prospects, and every confirmation — profile approval, card decisions, score overrides, and suggestion acceptance are yours.

What happens when evidence contradicts my product’s thesis?

Contradicting evidence is collected deliberately, stored with its source, and kept attached to the opportunity. It lowers evidence quality and confidence, and unresolved contradictions are shown alongside the recommendation instead of being filtered out. A use case that only survives when you hide the counter-evidence is exactly what the workflow is designed to catch.

Can I manually override a score?

Yes. Each dimension score can be overridden, because your team knows things public evidence does not. Overrides are recorded, and stale downstream conclusions — recommendations and unpublished reports — are invalidated and recomputed rather than silently kept.

Recommendations are versioned with the reason for the change, and decision reports keep the exact validation and analysis that caused it. If real feedback shifts the primary opportunity or its value proposition, you can trace afterwards what changed, when, and on which responses.

Can I export or share the results?

Decision reports can be shared through revocable public or password-protected links, and reports are immutable snapshots. The same sharing model applies to validation pages. PDF export is planned but not currently available.

What is the current status of UseCaseify, and how do I start?

UseCaseify is live in beta at usecaseify.com. You start by creating an account, entering your product’s URL or uploading materials, and confirming the product profile the system drafts. Official information is published on usecaseify.com and in this documentation repository at github.com/llmjp/usecaseify.

Try UseCaseify at usecaseify.com — 2 credits on sign-up, no credit card required.