Case Study
AI-Powered Assessment Platform
Automated language assessment pipeline powered by AssemblyAI (94%+ accuracy, 5.9% WER) and PyAnnote speaker diarization.
The Challenge
Manual Assessment at Scale
Language assessment organizations faced a critical bottleneck: each evaluation required a trained reviewer to manually listen, transcribe, analyze, and score audio recordings — a process taking 8+ hours per batch with inconsistent results.
Processing Time Comparison
Pain Points
- —Manual transcription prone to errors and fatigue
- —Inconsistent scoring across different reviewers
- —High operational costs limiting scalability
- —Slow turnaround times frustrating stakeholders
Business Impact
- —Limited throughput capping revenue growth
- —Quality variability affecting reputation
- —High labor costs eroding margins
- —Unable to meet growing market demand
The Solution
End-to-End AI Pipeline
A fully automated pipeline that processes raw audio recordings through five integrated stages — from ingestion to scored assessment — with human oversight at the final step.
Audio Input
Raw audio files uploaded
Processing
Noise reduction & separation
Transcription
Speech to text conversion
Analysis
AI quality assessment
Scoring
Automated evaluation
Fully Automated
From raw audio to scored assessment with zero manual intervention in the processing pipeline.
Human-in-the-Loop
AI handles the heavy lifting while human reviewers maintain quality control and final approval.
Modular Architecture
Each stage is independently scalable and upgradeable, allowing incremental improvements.
Technical Capabilities
Five Integrated Modules
A comprehensive system built from five specialized components, each optimized for its role in the assessment pipeline. Click any module to explore details.
Speaker Diarization
Powered by PyAnnote
State-of-the-art speaker diarization using PyAnnote, the leading open-source toolkit. Automatically separates and identifies speakers in multi-party conversations with high accuracy.
Speech Transcription
Powered by AssemblyAI
Industry-leading speech-to-text powered by AssemblyAI Universal-3 Pro. Achieves 94%+ accuracy with the lowest word error rate (5.9%) in the industry.
AI Quality Analysis
LLM-powered
Multi-dimensional quality assessment powered by large language models. Analyzes language accuracy, expression fluency, and professional terminology usage.
Automated Scoring
Consistent & objective
Standardized scoring engine ensuring consistency and objectivity across all assessments. Configurable scoring criteria with full audit trail.
Interactive Review
Human-AI collaboration
Human-AI collaborative review interface for efficient verification. Visual audio waveforms, one-click adjustments, and streamlined approval workflow.
Results
Measurable Impact
The platform demonstrates significant improvements across every dimension of the assessment workflow.
Privacy & Compliance
Privacy-Compliant by Design
Voice recordings are personal information under both Australia's Privacy Act 1988 and New Zealand's Privacy Act 2020. This platform is architected to meet both jurisdictions' requirements, following our Privacy-Compliant AI Development Whitepaper.
Click any item to explore the implementation details
All Data Stays in Australia
Two-Tier Deployment Model
Automated Scoring Explainability
Collect Only What Is Needed
72-Hour Response Capability
Access, Correction & Deletion
Self-Hosted Advantage
PyAnnote (speaker diarization) and Whisper (transcription) are open-source and run entirely on your own AWS infrastructure. Combined with Bedrock Claude for LLM analysis, the entire AI pipeline operates within ap-southeast-2 with zero data leaving Australia. This eliminates cross-border transfer concerns under APP 8 and IPP 12.
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