Letterly MVP Definition (Draft v3)
MVP Definition
Purpose of This Document
This document defines a focused MVP for the mid-late March 2026 beta launch (signal-gated). Its purpose is to align on who the MVP is for and what behavior we are testing, not to define the full Letterly vision.
This is a working draft and is expected to be corrected.
Note: Audience discovery happens before the MVP. Surveys and interviews are used to select the initial MVP audience. The MVP itself is designed to validate behavior within that selected audience, not to test multiple audiences simultaneously.
MVP Definition
For Letterly, MVP means the product is usable by beta users for one core communication use case. It does not require a full content library, perfect UX, or final pricing. This is a single end-to-end use case designed for validation-first learning, not the full Letterly ecosystem.
Target window: Late February 2026
MVP should be ready before the beta, so that beta users are testing something usable.
MVP is "usable." Beta is "testable."
MVP Goal
The MVP's goal is to validate whether the target audience will pay for short, live human practice sessions tied to a single course: "Tell Me About Yourself."
This is a validation-first MVP, not a platform build. The MVP must be “usable” before the mid-late March 2026 beta launch, meaning beta users will be testing something functional, not just a prototype.
Success is measured by learning whether this audience will pay for this experience, not by feature breadth or ecosystem completeness.
Target MVP Audience (Strawman)
The MVP targets high-stakes professional communicators who benefit from a strong, concise introduction:
Primary Audience: Non-English speakers working at U.S.-domiciled companies who are professionally and technically strong, but whose communication limits credibility. These individuals need a clear, confident introduction for interviews, onboarding, and first meetings.
Secondary Audience: Early-stage startup founders who need a tight personal introduction in conversations that build trust and credibility.
Both audiences share high motivation, clear stakes, and an immediate reason to practice speaking. This definition should be corrected if it does not reflect current demand.
Core Hypothesis
One core hypothesis will be tested:
1. The target audience will pay for a hybrid experience (AI feedback + optional human practice) when it helps them deliver a strong "Tell Me About Yourself" introduction.
This tests whether a single, high-stakes introduction scenario drives willingness to pay for AI feedback and human practice. The hybrid approach allows us to test the value of both components. If validated, broader audiences and formats can be layered on later.
What This MVP Is Testing
The MVP is designed to answer two primary questions:
1. Will the target audience choose to pay for AI feedback and optional human practice focused on "Tell Me About Yourself"?
2. What is the relative value of AI feedback versus human practice sessions for this use case?
Secondary signals we will observe include preferred session length, AI feedback usage rates, human practice session conversion rates, onboarding completion, and content access patterns.
What Is Explicitly In Scope
MUST HAVE
The MVP includes a single, narrow learning-to-practice loop centered on one course working end-to-end.
One clear course working end-to-end:
- Tell Me About Yourself
Users can:
- Access content (instructional or framing content designed to prepare learners for their use case)
- Receive AI feedback (even basic) on their communication practice
- Understand next steps (clear guidance on what to do after receiving feedback)
Clear onboarding explaining:
- What the product is
- What it is not (beta)
Product stability for basic use (the MVP must be usable, not just testable).
Additional components:
- A minimal amount of instructional or framing content designed to prepare learners for their specific use case. This content exists only to trigger practice behavior, not as a standalone offering.
- AI feedback capability (even basic) that provides users with feedback on their communication.
- Optional human practice sessions with a human coach focused on a professional outcome, with session types differentiated by audience need. These are available after AI feedback as an upgrade option.
- One simple scheduling flow (for human practice sessions).
- Basic post-session feedback collection (for human practice sessions).
What Is Explicitly Out of Scope
- K-12 students and cohort bootcamps may continue separately but are not part of the MVP learning loop.
- Subscription models, virtual currency, and marketplace optimization are intentionally excluded.
- High-production video libraries and advanced analytics are deferred.
- Parallel learning flows beyond the single core course are deferred.
MAY HAVE (Not Required)
- Polished UI (basic UI is sufficient for MVP usability)
Success Criteria
The MVP is successful if a meaningful portion of beta users:
- Complete onboarding
- Access content for their use case
- Receive and engage with AI feedback
- (Optional) Complete at least one paid live practice session
Strong signals include:
- Onboarding completion rates
- Content access and engagement
- AI feedback usage and perceived value
- Conversion from AI feedback to human practice sessions
- Repeat purchases (for human practice)
- Feedback that clearly links the experience to increased confidence or readiness for real-world situations
A lack of willingness to pay, a clear preference for AI-only tools, or low engagement with either component are all valid and valuable outcomes. Learning which components (AI feedback vs human practice) provide more value is equally valuable.
Guiding Principle
This MVP exists to reduce uncertainty around paid human practice for a single, high-stakes introduction scenario.
Every feature included must directly support learning whether this audience will pay for this experience. Anything else should wait.
Reference: 2025-09-19 Product Roadmap — documents the core problem this MVP addresses.
Related Views
This is a working draft on Jan 8, 2026 and is expected to be corrected.