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Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Singh
Takeaway
Real contact-center voice AI ROI comes from automating the 1:1 after-call work via a low-latency STT + structured-output LLM + CRM-schema-mapper pipeline with a human verification step.
Summary
- Fujitsu's contact-center pipeline: 6.5-min average call, 6.3-min after-call work (ACW) — automating ACW is the explicit ROI target (~50% time saved).
- Four stages: voice capture (channel-split stereo agent/customer, noise filtering, PII masking), STT (target >90% accuracy, domain dictionaries, ITN/auto-punctuation), GenAI core (few-shot prompts producing separate bullet lists per intent, classification with reasons, hallucination checks), and API gateway acting as schema mapper to CRM.
- Operator stays in the loop — reviews auto-populated summary, edits, confirms — so structured data flows into BI without removing human judgment.
- Frames the operator stress / retention spiral as engineering problem solvable by reducing admin load.
voice-aicontact-centerstt
Original description
"Processing real-time voice data is an engineering minefield of latency, accents, and interruptions. This session explores the architecture of a Real-Time Voice Intelligence Pipeline deployed in a high-volume contact center. We will move beyond simple transcription to discuss Structured Intent Extraction. I will show you how to design: 1. Voice Capture Pipeline: The entry point for clean, multi-channel data acquisition. 2. Speech-To-Text(STT) Engine: Converting speech to accurate text. 3. Generative AI Core Structure: Using rigorous system prompts to force the LLM to separate ""Customer Intent"" from ""Operator Chit-Chat"" and output valid JSON, even from garbled transcripts. 4. Customer Data Sync: Translating AI insights into enterprise system actions. We reduced post-call work by 50% by shifting compute from ""batch"" to ""stream."" Speaker: Dippu Kumar Singh - Leader Of Emerging Technologies (Apps), Fujitsu North America Inc. Dippu Kumar Singh has over 16 years of experience at the intersection of industry innovation and advanced research. He is a recognized authority in building scalable, trustworthy, and commercially viable AI systems. Being a Leader for Emerging Data & Analytics at Fujitsu North America, Dippu specializes in bridging the gap between theoretical AI concepts and enterprise-grade implementation. His strategic leadership has spearheaded multi-million in sales pipelines and delivered remarkable savings through AI-driven optimizations in transportation, manufacturing, utilities, and supply chain logistics. Socials: https://www.linkedin.com/in/dippukumarsingh/ Slides: https://docs.google.com/presentation/d/1f2y1s64irhdDNTRgK6bWrBtOgMWlhQYM/edit?usp=sharing&ouid=107532212133041789455&rtpof=true&sd=true"