Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams
summary
A deployed four-stage pipeline captures multi-channel audio with early PII masking and channel separation, feeds an STT engine tuned for >90% accuracy and inverse text normalization, then runs a prompt-orchestrated LLM to emit strict JSON intent + confidence (few-shot templates, hallucination checks, token optimization), and maps fields through an API gateway into CRM with an operator verification step. Reported outcome: after-call work drops ~50% across 500 seats and data quality becomes standardized.
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chapters (13)
- 0:00Voice AI in contact centers
- 1:56Contact center challenges
- 5:07After-call work inefficiencies
- 6:53Low-latency pipeline overview
- 7:57Audio preprocessing
- 10:07Speech-to-text requirements
- 11:32Generative AI summarization
- 12:58CRM data sync
- 14:23Data pipeline logic
- 15:50Operational impacts and ROI
- 17:35Engineering constraints
- 19:41AI advancements roadmap
- 22:10Closing
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