Sold by: PolyAI
Deployed on AWS
Omnichannel conversational AI agents that resolve customer enquiries across voice and chat, understanding every caller regardless of accent, dialect, or language. Built on Raven, PolyAI's proprietary customer-service LLM, and deployed by enterprises handling up to 90% of customer calls without a human agent.
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Overview
PolyAI is the conversational interface for the enterprise: omnichannel AI agents built for complex, high-stakes customer interactions across voice and chat. Enterprise deployments handle up to 90% of customer calls without a human agent, powered by Raven, PolyAI's proprietary customer-service LLM.
Callers speak naturally: interrupting, asking questions, and moving between topics, without guessing keywords or repeating themselves. Raven is purpose-built for customer service, not a general-purpose chatbot, and is post-trained on real production conversations from 130+ enterprise deployments, including the accents, background noise, and disfluencies a generic model never sees.
Raven answers in ~320ms median, among the fastest models built for real-time voice, and leads competing real-time models on safety. Self-hosted on PolyAI-managed infrastructure rather than billed per token through a frontier API, it carries a structural cost advantage. Customers aren't locked in: Raven is the optimized default, but agents can run on Gemini, GPT, or another approved model with the same guardrails and tools underneath.
PolyAI powers 2,000+ live agent deployments for enterprises including FedEx, Marriott, Caesars Entertainment, Volkswagen, and Nestle, processing 100M+ calls a year across 100+ languages and locales (45 with full voice support). The platform is ISO/IEC 27001:2022 and Cyber Essentials Plus certified, SOC 2+ Type 2 audited, HIPAA (BAA available) and PCI DSS controls in place, and GDPR-ready with EU residency.
Highlights
- Purpose-built for customer service. Raven, PolyAI's proprietary LLM, is post-trained on real production conversations from 130+ enterprise deployments, not general web data. Within the real-time group, it leads competing third-party models on safety and responds in ~320ms median first-token latency. Callers speak naturally, interrupting and switching topics without guessing keywords, while Raven understands accents, dialects, and background noise that trip up generic models.
- Omnichannel, no lock-in. One agent runs across voice and chat with 130+ pre-built, CCaaS-agnostic connectors. Raven is the optimized default, but you can run Gemini, GPT, or another approved model underneath with the same guardrails and tools.
- Proven at enterprise scale. 2,000+ live agent deployments processing 100M+ calls a year, handling up to 90% of customer calls without a human agent. Recognized in Gartner's MQ for CAIP, the Forrester Wave for Conversational AI, and Everest Group's Voice AI Agents report.
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Dimension | Description | Cost/12 months |
|---|---|---|
500K Minutes - 12 Months | Minimum annual commitment for 500,000 minutes of usage | $175,000.00 |
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100
In Natural Language Processing
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25
In Speech Recognition
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100
In Natural Language Processing
Sentiment is AI generated from actual customer reviews on AWS and G2
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AI generated from product descriptions
Proprietary Language Model for Customer Service
Raven, a proprietary large language model post-trained on real production conversations from 130+ enterprise deployments, purpose-built for customer service with optimization for accents, dialects, and background noise recognition.
Real-Time Voice Processing
Median first-token latency of approximately 320ms for real-time voice interactions, enabling natural conversation flow with support for interruptions and topic switching without keyword guessing.
Omnichannel Deployment Architecture
Single agent deployment across voice and chat channels with 130+ pre-built, CCaaS-agnostic connectors, supporting flexible model selection including Raven, Gemini, GPT, or other approved models with unified guardrails and tools.
Multilingual and Multilocale Support
Support for 100+ languages and locales with 45 languages featuring full voice support, enabling deployment across diverse geographic regions and customer bases.
Enterprise Security and Compliance
ISO/IEC 27001:2022 and Cyber Essentials Plus certification, SOC 2+ Type 2 audit compliance, HIPAA controls with BAA availability, PCI DSS controls, and GDPR readiness with EU residency options.
Robotic Process Automation Integration
RPA design and execution modules integrated within the platform for automating business processes
Pre-built Industry Templates
Over 80 industry cartridges available out of the box that can be customized for organizational requirements
Multi-channel Conversational Interfaces
Support for building virtual agents, chatbots, voice assistants and other conversational AI solutions
Analytics and Performance Monitoring
Analytics modules for tracking and monitoring conversational AI solution performance and interactions
Enterprise System Integration
Ecosystem of integrations with customer relationship management software and other enterprise platforms to connect legacy, hybrid and cloud elements
Multilingual and Multi-Channel Support
Pre-trained AI agents capable of supporting 100+ languages across 30+ voice and digital channels with over 100 prebuilt integrations
Multi-Model LLM Orchestration
Support for multiple large language model vendors including Amazon Bedrock, OpenAI, Azure OpenAI, Anthropic, Cohere, Google, and Aleph Alpha
AI-Powered Knowledge Management
Semantic search and Generative AI-based knowledge management system for delivering contextual and accurate answers to customer inquiries
Conversational and Generative AI Integration
Integration of Conversational AI and Generative AI technologies to create Agentic AI capabilities for autonomous agent operations
Pre-trained Industry-Specific Skills
Pre-configured AI agents with industry-specific skills and common service processes ready for deployment
Standard contract
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Customer reviews
JaiBharath Boithi
Virtual agents have transformed routine customer calls and now free my team for complex issues
Reviewed on Sep 20, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for PolyAI is to build a solution for customer service, particularly automating common queries efficiently.
I use PolyAI to handle common customer service calls such as account balance checks and basic requests. For example, my virtual agent can understand a customer's request, provide the relevant information, and hand the conversation over to a human agent when needed.
It has made it easier for my customers to get the information they need by making the process more efficient.
PolyAI fits well into my workflow by automating repetitive customer interactions while allowing my support team to focus on more complex requests.
What is most valuable?
Key features of PolyAI that stand out for me are its natural language understanding, my ability to handle multiple customer calls, and a smooth handoff to a human agent when needed.
PolyAI has helped me reduce the workload from repetitive queries and improve response times.
Another great feature of PolyAI is its analytics and reporting, which helps me understand customer interactions.
Overall, PolyAI has been a great addition to my customer service workflow, and its strong natural language understanding, voice quality, and automation have helped improve efficiency.
What needs improvement?
One area where PolyAI can be improved is in deeper integrations with existing systems and more configuration on how to handle complex scenarios.
An area for improvement with PolyAI would be more flexibility to fine-tune conversation flows and responses.
Another improvement would be the advanced analytics and reporting of PolyAI. Better visibility into conversational trends and customer behavior would help my teams identify improvement opportunities more quickly.
For how long have I used the solution?
I have been using PolyAI for six months.
What do I think about the stability of the solution?
In my experience, PolyAI is stable and secure.
What do I think about the scalability of the solution?
PolyAI's scalability for my use case can handle increasing customer call volumes without significant impact.
How are customer service and support?
PolyAI's customer support has been responsive and helpful, assisting with setup, integration questions, and troubleshooting.
Which solution did I use previously and why did I switch?
Before PolyAI, I used a more basic IVR solution, and I switched because I needed its natural language understanding, more flexible conversations, and a smoother handoff for complex customer requests.
How was the initial setup?
PolyAI's initial setup requires some integration.
What was our ROI?
I have seen a return on investment with PolyAI through time savings and reduced workload for my teams, allowing my support to focus on high-value issues.
What's my experience with pricing, setup cost, and licensing?
PolyAI's pricing and licensing were competitive for my use case.
Which other solutions did I evaluate?
I evaluated a few other options before choosing PolyAI.
What other advice do I have?
My advice to others looking into using PolyAI is to clearly define customer service use cases and integration requirements before starting. My overall review rating for PolyAI is 8 out of 10.