Technical portfolio project
AI-Powered Call-Center Audio QA and Campaign Analytics Platform
End-to-end system that ingests large call volumes, generates transcripts and speaker roles, scores compliance and KPIs, and delivers interactive campaign analytics.
This entry documents MuFaw research and technical work. It is not presented as a paid client engagement unless the project page explicitly states otherwise.
Project details
What we delivered
Overview
End-to-end pipeline to ingest inbound, outbound, and bot calls, then generate transcripts and QA signals at scale.
Interactive dashboards provide campaign-level drilldowns and exportable reports in minutes.
Key Features
- Automatic transcript, diarization, voice activity detection, and speaker role classification.
- Prompt-engineered Vertex AI workflows for intent, sentiment, compliance, and KPI extraction.
- Scoring engine for bot handoffs, script adherence, SLA breaches, and custom rules.
- Multi-tenant React dashboard with filters, drilldowns, and exports.
- High-throughput batch and streaming processing for thousands of calls per minute.
Architecture & Tech Stack
- Inference: Vertex AI LLMs with prompt orchestration and custom NLU models.
- Backend API: FastAPI for ingest, orchestration, and auth.
- Processing: distributed workers with Kafka or SQS queues and STT engines.
- Storage: object storage for audio, FAISS or vector DB for semantic search, relational metadata store.
- Frontend: React analytics dashboard.
Impact / Outcomes
- Automated QA reduced manual review time by orders of magnitude.
- Clear KPI visibility enabled bot tuning and agent coaching with measurable ROI.
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