Practice job interviews by actually talking. A real-time AI interviewer scores every answer, tracks your filler words and pace — then hands you a full report card with a hire signal.
Human interview coaches charge $100–250/hour. Most candidates get zero structured practice before the real thing.
Reading answers in your head trains recall — not speaking coherently under pressure, which is what actually fails in interviews.
Friends flake. Peers can't judge a FAANG loop. Feedback, when it exists, is vague — no scores, no data, no progress tracking.
Pick a role, an interviewer persona, and just talk. MockMate runs a real voice conversation — semantic turn-taking, barge-in, follow-up probes — then scores every answer and your delivery, live.
Paste your CV — the interviewer asks about your actual projects and challenges your claims.
Every answer scored 1–10 via tool calls; filler words and WPM measured from the live transcript stream.
Session history with score deltas: “▲ +8 vs last attempt”. Practice becomes a game with a scoreboard.
Streaming captions, question tracker, score chips, report card with PDF export.
AudioWorklet mic → 24 kHz PCM16
gapless playback + barge-in flush
tool handlers drive the UI
STT (Universal) + LLM routing
+ turn detection + TTS
through ONE connection
Python stdlib only
GET /v1/token proxy
serves the app
Short-lived single-use tokens; the API key never leaves the server.
plan_interview · log_question · score_answer · end_interview — JSON-Schema function tools sync the interviewer's brain to the UI.
Résumé-extracted keyterms, transcription prompts, Hinglish code-switching, session.resume.
| Feature | How MockMate uses it |
|---|---|
| Client-side tools | 4 JSON-Schema tools drive question tracking, live scoring & the final report — the agent literally operates the app |
| Inline session config | Role × persona × level compose a bespoke system prompt, greeting & voice per session — no stored agent needed |
| Keyterm boosting | Résumé text is scanned (60-term tech lexicon) → STT keyterms — “Redis” never becomes “read us” |
| Turn detection & barge-in | Semantic end-of-turn (thinking pauses are safe) + instant playback flush on interruption |
| Transcript streaming | User deltas → live captions & filler/pace analytics; agent word deltas → karaoke captions |
| Tokens & resume | Single-use 5-min tokens minted server-side; session.resume reconnects dropped calls |
| Voices & languages | 11 selectable voices, persona-matched defaults; Hindi-English code-switched input via language_codes |
Beachhead: job-seeking students & career switchers (🇮🇳 angle: non-native speakers get Hinglish support). Expansion: campus career cells, bootcamps, HR screening tools.
The interviewer doesn't just talk — it operates the application through tool calls. Résumé-grounded questioning + delivery analytics fused into one report.
Saved question banks → industry-specific loops → Twilio phone interviews → team dashboards for bootcamps.
MIT license, zero-dependency backend, one-click Render deploy — fork it and practice tonight.