LP.
SYSTEM CASE STUDY

AI Interview Analyst

End-to-end system that transcribes interview recordings, extracts key insights, and generates structured analysis reports. Audio processing, speaker diarization, and LLM-powered summarization.

PythonWhisperGPT-4FastAPIReact
VERIFIED BENCHMARKS & IMPACT
Benchmark

60min audio → report in <2min

Benchmark

90%+ speaker diarization accuracy

Benchmark

Structured scoring across 12 dimensions

01 / THE PROBLEM

Operational Bottleneck

Hiring teams spend hours manually reviewing interview recordings, taking notes, and comparing candidates. The process is slow, subjective, and inconsistent.

02 / SYSTEM ARCHITECTURE
01

Audio upload → Whisper transcription with timestamps

02

Speaker diarization to separate interviewer vs candidate

03

LLM chain for insight extraction: skills, red flags, culture fit

04

Structured JSON output for consistent scoring across candidates

05

FastAPI backend with async processing queue

06

React dashboard for side-by-side candidate comparison

03 / RESULTS & PRODUCTION IMPACT
-Full audio-to-report pipeline completes in under 2 minutes
-Structured JSON output enables automated candidate scoring
-Speaker diarization at 90%+ accuracy

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