From Idea to MVP: Building an AI-Powered Mock Interview Platform
How an AI interview platform went from concept to working MVP, and the decisions that shaped its product engineering along the way.
Practising for interviews works best with realistic questions and specific feedback. This case study follows how an AI mock interview platform moved from concept to a working MVP.
Defining the core experience
The MVP focused on one loop: choose a role, answer generated questions by voice or text, and receive structured feedback. Features such as progress dashboards and social sharing were deferred until the core loop proved useful.
How AI powers it
A language model generates role-specific questions from a job description. Speech-to-text transcribes spoken answers. The model then evaluates each answer against a rubric covering relevance, structure, clarity and use of examples, and returns concrete suggestions.
Product engineering decisions
Rubric-based prompts made feedback consistent and explainable. Response streaming kept the experience responsive. Storing transcripts and scores allowed the team to review feedback quality and refine prompts with real data.
Validating the MVP
Early users were asked whether feedback felt accurate and actionable, and whether they returned for another session. Those signals decided which features to build next.