MockMate

A full-stack AI interview platform. Paste a job description, answer tailored technical questions from an AI interviewer, and receive a structured graded report. Built with Next.js, Google Gemini, Prisma, and AWS Lambda for background processing.

Interview prep tools ask you to read, not do. The real problem was designing an AI pipeline that ingests any job description, generates role-specific technical questions, and grades answers the way a hiring panel would, with depth, clarity, and gap analysis, not just correct or incorrect.

Next.js App Router for full-stack delivery in one repo. Google Gemini via the Vercel AI SDK for streaming question generation and answer grading. AWS Lambda for heavy grading jobs so the UI never blocks. Prisma for a typed data layer. PDF.js to parse uploaded resumes. PostHog to see where users drop off.

Built an AI pipeline: JD upload, Gemini parses role requirements, generates targeted questions, streams answers, and grading runs in AWS Lambda with structured Zod-validated output. Two decisions worth calling out. First, I split the AI into two separate flows, a live interview conversation and a separate grading pass. If I had merged them into one prompt, the feedback came out inconsistent, because the model was doing two jobs at once. Second, answers persist to the database before any AI runs. If a Gemini or Lambda call fails, the user's work is still there instead of vanishing mid-interview.