Overview

Product video
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 75 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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Customer reviews
Automation has reduced routine inquiries while agents have gained time for complex issues
What is our primary use case?
Our main use case for PolyAI revolves around supporting workflows and cloud reliability, as we are using PolyAI's omni-channel platform for centering our four pillars: architectural resilience, security boundary integrity, rapid deployment velocity, and operational efficiency. It is also helping us to deploy great things like an unstoppable velocity; PolyAI platform is a valuable category across evaluations by automating routine high-volume Tier 1 customer inquiries across voice and chat. Our first point of contact spikes while human escalation queues shrink dramatically, resulting in operational cost reduction and faster meantime-to-resolution time improvements, with compounding ROI justifying the platform investment to executive leadership.
PolyAI has changed the way our team handles Tier 1 consumer inquiries by offloading operational queues and first contact resolution. From a DevOps and infrastructure perspective, we required an enterprise-grade solution that could cleanly interface with our core backend microservice, CRM data planes, and telephony backbones without introducing legacy spikes or fragile bot maintenance overhead. PolyAI's omni-channel conversational platform has provided us with both voice and digital chat touchpoints, automating our routine high-volume inquiries system.
How has it helped my organization?
PolyAI has positively impacted our organization significantly. I have seen a very great impact, with a 330% ROI and fast payback aligned with independent benchmarks such as Forrester TEI studies. We have achieved full payback within the first six months, and the projected three-year ROI exceeds 600%, yielding millions in operational efficiency in our contact and support operations. We have also seen a 5.33% call and chat containment, as the platform reliably automates fully standard Tier 1 inbound requests across voice and digital channels without requiring human intervention. The unit cost reduction has been cut by 93.83%, with PolyAI handling routine queries costing us around 25 cents to 50 cents per interaction compared to traditional human agent costs of $7 to $12, effectively slashing our overall cost per contact by almost 93.8%. The capacity and SLA recovery 24/7, especially during off-peak windows and sudden incident spikes, has led to an 83.88% drop in call abandonment, with average queue times halved, thus freeing human engineers to focus strictly on high-severity and escalated complex issues.
The implementation of PolyAI has boosted the overall morale of our human agent team since they can now focus on complex and non-solvable issues. This motivates them as they engage in research and complex problem-solving rather than answering repetitive queries. It alleviates the monotony they previously faced, allowing them to utilize their skills more effectively.
What is most valuable?
The best features PolyAI offers include its professional service as a software approach, which is a significant differentiator during initial onboarding, with the structured questionnaire process capturing our architecture, security constraints, brand voice, and API handshakes down the wire. They work instead of handling a raw, self-serve bot builder, allowing our platform engineers to avoid constant babysitting while PolyAI's dialogue engineers handle model fine-tuning, edge case routing, and continuous optimization. I also appreciate the low-latency, human-parity voice system, which helps the voice agent handle non-linear speech, sudden conversational pivots, background noise, and mid-sentence interruptions naturally, making audio latency practically imperceptible and eliminating the awkward pauses typical to traditional conversational IVRs. This is a game-changer. Finally, I appreciate how moving between chats and voice maintains context seamlessly, as conversations trigger or fallback or human handoff. State payloads transfer cleanly with zero loss of session context.
The low-latency and human-parity voice system make a significant difference for our team, ensuring that interactions between AI and humans do not sound robotic. This capability makes the AI's responses clear, without context lagging or hallucination. Our end user experience has positively increased by 23.5%. Earlier, latency posed a challenge, but low latency ensures that we can achieve our end goals efficiently.
The governance and security of PolyAI is really strong. It has not missed anything in terms of governance and AI, which I feel has been handled really well.
The accuracy and reliability of PolyAI's AI output is exceptional, more than 200%, since it does not hallucinate, provides long answers without losing context, and utilizes low latency and advanced AI models. They have done a good job in this aspect.
What needs improvement?
I feel that PolyAI already provides us with everything needed in our situation, and its overall capabilities are really great, surpassing our expectations in terms of ROI. Thus, I believe there is no particular improvement I would suggest at this time.
For how long have I used the solution?
I have been using PolyAI in our organization for the last one and a half years.
What do I think about the stability of the solution?
PolyAI is definitely a stable solution.
What do I think about the scalability of the solution?
PolyAI is a very scalable solution; I would rate it a ten out of ten in terms of scalability, as it handles large volumes of data easily.
How are customer service and support?
The customer support is really good; they are always available, although we have not had many queries. I had an interaction with them six months back, and they resolved my query within 30 to 40 minutes. I would rate the customer support a ten out of ten.
Which solution did I use previously and why did I switch?
We did not use a previous solution; this is our first time implementing such a system.
How was the initial setup?
It was pretty easy to deploy PolyAI in our environment, with the direct deployment assisted by PolyAI's team. The initial deployment and configuration were completed within a week. Integrations with other AWS services are really straightforward and smooth; we have not faced any challenges or lags during the integrations.
What about the implementation team?
We were able to configure the solution easily into our systems, with end-to-end support from PolyAI's team and our internal teams, all accomplished within four days after we received the solution.
What was our ROI?
I have achieved a 330% ROI with fast payback aligned with independent benchmarks such as Forrester TEI studies. We have achieved full payback within the first six months, and the projected three-year ROI exceeds 600%, yielding millions in operational efficiency in our contact and support operations. The unit cost reduction has been cut by 93.83%, with PolyAI handling routine queries costing us around 25 cents to 50 cents per interaction compared to traditional human agent costs of $7 to $12, effectively slashing our overall cost per contact by almost 93.8%.
What's my experience with pricing, setup cost, and licensing?
The metering and billing experience is handled clearly with transparent pricing, setup cost, and licensing. Everything aligns with what was discussed earlier; there is no particular feedback on that part.
Which other solutions did I evaluate?
I did evaluate some other options before choosing PolyAI, including Sierra, Tekagon, Cresta, Genesys Cloud CX, Five9 IVA, and AWS Lex.
What other advice do I have?
I would rate PolyAI a five out of five. I choose five for my rating because the ROI and everything I have highlighted is exceptional and deserves the highest score. Anyone looking for an exceptional solution for their company should definitely consider PolyAI as an alternative to their current product.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Virtual agents have transformed routine customer calls and now free my team for complex issues
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.