Strategic Rationale
Why This Audience First
The MVP focuses on two distinct audiences: non-English speakers working at U.S. companies, and early-stage startup founders. This is an intentional choice, not an oversight. These audiences were selected through pre-MVP surveys and interviews that identified highest-intent users. Here's why this selection matters.
High Motivation, Clear Stakes
Non-English speakers at U.S. companies face communication barriers that limit their credibility despite technical and professional strength. Early-stage founders need to secure their first check and find angels. Both have immediate, tangible reasons to practice: career advancement, credibility, and funding depend on their communication skills.
This isn't abstract learning. It's preparation for specific, high-stakes situations with clear outcomes. That motivation translates directly to willingness to pay for quality practice.
Clear Value Proposition
When someone is preparing for workplace communication that affects their credibility, or a pitch that determines whether they get funding, the value of feedback is obvious. They can directly connect AI feedback and optional human practice to their goal: building credibility or securing investment.
This clarity makes it easier to validate whether people will pay. The value isn't theoretical—it's tied to specific, upcoming events with real consequences. By focusing on one use case per audience (workplace communication for non-English speakers, pitching for founders), we simplify validation while maintaining clear value.
Focused Testing
By focusing on two related but distinct audiences with high-stakes communication needs, each with one clear use case, we can test whether professionally motivated learners will pay for AI feedback and optional human practice. Both audiences share the core need: improving communication for career-critical outcomes.
While we're serving two audiences, they're united by a common value proposition: AI feedback plus optional human practice for high-stakes professional communication. This hybrid approach allows us to test the value of both components while learning which segment (if any) shows stronger engagement. By narrowing to one use case per audience, we simplify testing while maintaining dual validation. If we tried to serve completely unrelated audiences or multiple use cases per audience—K-12 students, casual learners, professional prep—we'd be testing multiple hypotheses at once, making it harder to learn what works.
Market Validation
Both audiences represent clear market segments with demonstrated demand for communication support. Non-English speakers at U.S. companies are already investing in professional development focused on workplace communication. Startup founders are already spending on pitch coaching and fundraising support.
By focusing on these two audiences first, each with one clear use case, we validate whether AI feedback and optional paid human practice is a viable product for high-stakes professional communication. This validation phase comes before any platform expansion or scaling decisions.
This Doesn't Mean Exclusion
Choosing these two audiences first doesn't mean other learners aren't important. It means we're starting with the clearest test cases for high-stakes professional communication.
If this MVP validates the core hypothesis—that people will pay for AI feedback and optional human practice for high-stakes professional communication—then we can expand to other audiences, use cases, and formats in post-MVP phases. But we need to validate the foundation first. The MVP is not a stepping stone to subscriptions, marketplaces, or scaling—those are conditional next steps.
The Bottom Line
These two audiences, each with one clear use case, give us the best chance to learn whether AI feedback and optional paid human practice is a viable product for high-stakes professional communication. They have clear motivation, obvious value, and immediate need.
Once we observe and learn whether this works—and which audience segment (if any) shows stronger engagement, and which components (AI feedback vs human practice) provide more value—we can decide on next steps. But we need to start somewhere clear and focused for validation, and this is it.