PolyAI : Multilingual Voice AI Assistant for Scalable, Human-Like Customer Service

General AI SaaS Platforms

PolyAI (UK) – Voice AI Assistant for Enterprise Customer Service

PolyAI worked with us to develop voice-first AI assistants that can handle customer service calls with near-human fluency. Using state-of-the-art speech recognition and intent understanding, the assistant routes calls, answers questions, and resolves issues without ever needing to escalate. It’s designed for enterprise-scale usage—capable of processing accents, emotions, and interruptions across 30+ languages. Deployed in industries like telecom and retail, the AI significantly reduces call center volumes while maintaining customer satisfaction. It’s a plug-and-play enterprise voice solution powered by conversational intelligence.

Project Overview

Client: PolyAI (UK leader in voice AI solutions for Fortune 500 call centers)
Challenge: High support costs and limited scalability of human-staffed call centers
Objective: Develop a production-ready voice AI system to:
- Understand and respond to voice-based customer queries with human-like tone
- Handle call routing, query resolution, and interruptions in real time
- Support multilingual deployments with accent adaptation and emotion detection
Team: 10 (3 Speech Engineers, 3 NLP Experts, 2 Backend Devs, 1 Linguist, 1 PM)
Timeline: 6 months (Voice Model Dev → Industry Pilots → Scalable Cloud Deployment)

“GenX helped us turn AI voice assistance into a brand ambassador—fluent, empathetic, and always ready.”

Head of Voice AI, PolyAI

The Challenge

Critical Pain Points:

Human agents found it difficult to handle high volumes and different accents
Wait times and call escalations hurt customer satisfaction scores
Existing IVRs lacked natural interaction and adaptive understanding

Technical Hurdles:

Building voice models that handle speech variability, emotional tone, and background noise
Creating fallback logic that maintains conversation flow without sounding robotic
Ensuring secure, low-latency voice processing at scale across regions

Tech Stack

Component	Technologies
Speech Recognition & ASR	OpenAI Whisper, DeepSpeech, wav2vec 2.0
NLP & Intent Understanding	BERT, Rasa, spaCy, HuggingFace Transformers
Dialogue Management	RNNs, Reinforcement Learning, LangChain, Node.js
Multilingual Processing	Fairseq, Polyglot, DeepL APIs
Infrastructure & Telephony	Twilio, WebRTC, Kubernetes, Redis
Monitoring & Voice Analytics	Prometheus, Grafana, CallMiner

Key Innovations

PolyAI managed fluent conversations in 30+ languages, detecting accents and emotions. It resolved issues without agent escalation, even with interruptions. Businesses saw lower call volumes and higher customer satisfaction.

Interrupt-Resilient Dialogue Flow

Seamlessly resumed context after caller interruptions or noise

Result: 44% drop in caller hang-ups due to frustration

Real-Time Emotion-Aware Routing

Escalated calls automatically when anger or distress was detected

Result: 31% improvement in CSAT for sensitive support lines

Accent-Agnostic Speech Recognition

Consistently understood regional dialects and pronunciation styles

Result: 92% voice intent accuracy across 7 countries in telecom deployment

Our AI/ML Architecture

Core Models

Voice-to-Intent Recognition Engine:
- End-to-end deep learning pipeline using Whisper + BERT for semantic intent mapping
- Handles accents, code-switching, and overlapping speech
Natural Dialogue Manager:
- RNN + rule-based hybrid for multi-turn conversations with fallback and sentiment recovery
- Interrupt detection, emotional routing, and context memory
Multilingual Voice Adaptation Module:
- Handles seamless transitions between 30+ spoken languages and variants
- Supports dynamic switching between 30+ languages and dialects

Data Pipeline

Sources
- Voice transcripts from past calls, call logs, knowledge bases, escalation tags
- Multilingual audio corpora with labeled emotion and intent