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Architecture ExpertiseAI PlatformsVideo Streaming SystemsML PipelinesReal-Time Processing

AI Interview Platform System Architecture

Designing scalable video interview platforms with AI analysis, real-time transcription, and automated candidate scoring.

System Overview

An AI-powered interview platform combines real-time video streaming, speech-to-text transcription, and machine learning models to evaluate candidate responses.

The architecture typically includes video ingestion services, real-time processing pipelines, AI scoring engines, storage for interview recordings and transcripts, and analytics dashboards for recruiters.

Key Components

Video Ingestion

Handles video capture, encoding, streaming, and storage using WebRTC or cloud video services.

AI / ML Processing

Processes transcripts to evaluate sentiment, confidence, communication skills, and behavioral indicators.

Transcription Service

Converts interview audio to text using speech-to-text models or cloud AI services.

Storage

Video recordings, transcripts, candidate data, and metadata in blob storage and databases.

Analytics Dashboard

Recruiter insights, candidate scores, and reporting dashboards.

API Gateway

Single entry point for frontend, candidate sessions, and recruiter APIs.

Architecture Diagram

Interview Processing Flow

  • 1Candidate starts interview session
  • 2Video stream captured via WebRTC
  • 3Audio processed by speech-to-text service
  • 4Transcript sent to ML scoring models
  • 5Scores stored in analytics database
  • 6Recruiter dashboard displays candidate insights

Technology Stack

Video Infrastructure

WebRTCAgoraTwilioCloud Video Services

AI / ML

OpenAIAzure AIPython ML PipelinesTensorFlowPyTorch

Backend

.NETNode.jsMicroservices

Data

PostgreSQLMongoDBBlob StorageRedis

Scalability Considerations

  • Video streams handled using scalable WebRTC gateways
  • AI scoring pipelines processed asynchronously
  • Event-driven architecture for transcript processing
  • Distributed storage for interview recordings
  • Horizontal scaling of scoring services

Challenges

  • Low latency video streaming
  • Real-time transcription accuracy
  • AI model bias and fairness
  • Handling large video storage volumes
  • Scaling concurrent interview sessions

Real World Use Cases

  • Automated candidate screening
  • Technical interview analysis
  • Soft skill evaluation
  • Remote hiring platforms
  • University placement systems

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