August 7, 2025
5 min read
Guest Blogger
PolyAI uses retrieval-augmented generation to tackle AI hallucinations, ensuring accurate, trustworthy voice AI for enterprise customer service.
AI Hallucinations Are a Roadblock. Here’s How PolyAI Is Helping Enterprises Push Past Them
By fusing retrieval-augmented generation with voice AI, PolyAI is tackling one of the biggest threats to enterprise trust in automation: chatbot hallucinations. It’s undeniable that hallucinations are one of the main sticking points when it comes to the mass rollout of AI technology. This can be a roadblock for enterprises looking to implement AI, especially when there are constant reports about these hallucinations hitting the headlines. While some hallucinations are comical—like when a Virgin Money customer was warned against hateful speech for using the word “virgin”—others have more serious consequences. For example, Cursor, an AI-powered software coding assistant from startup Anysphere, went viral after its chatbot hallucinated a policy that did not exist. When customers were logged out of their accounts and asked for help, the chatbot named ‘Sam’ falsely claimed this was “expected behavior” under a new policy it had invented. This caused confusion, distrust, and even cancellations. Large Language Models (LLMs), which underpin many chatbots, are powerful but prone to such hallucinations if not properly constrained. Speaking to CX Today, Nikola Mrkšić, CEO and Co-Founder of PolyAI, explained how his company has “been able to constrain the behavior of LLMs and use them in places where it makes the most sense to drive customer service conversations.” PolyAI helps enterprises engage customers through voice AI agents that not only speak but also do the right things—avoiding hallucinated responses or false claims of action. Some of PolyAI’s guardrails rely on retrieval-augmented generation (RAG), a technique that enables AI agents to cross-reference generative model outputs with a verified knowledge base. This ensures responses are factual, relevant, and appropriate, preventing inaccurate or irrelevant answers and keeping conversations within established boundaries.Where Enterprises Can Make a Difference Today with AI
In pursuit of seamless customer experience (CX), businesses must assess how AI and automation impact accuracy, trust, transparency, operational costs, and efficiency. Nikola Mrkšić advises enterprises to consider sophisticated voice AI agents as a strong starting point for AI deployment in CX. He said, “AI is such a big and monumental thing, and many people can’t resist mounting these large offensives. What they need is a lot of probing attacks on different front lines.”Large cloud providers or CCaaS vendors will tell enterprises to start with Agent Assist capabilities, and then down the road think about automation, but we take a different approach.Mrkšić also noted that while AI assistance for human agents brings benefits, it may not support broader automation goals. “The model proposed by CCaaS vendors will not lead to the future these enterprises need, where 90% of calls are automated.”
How PolyAI Is Addressing the Challenges of Providing Good CX through Voice AI
Voice interactions remain central to CX but face challenges such as:- Latency and speech recognition errors: Delays and misinterpretations can frustrate customers.
- Lack of contextual awareness: AI may struggle with complex queries requiring historical context.
- Limited conversational flexibility: Rigid scripts reduce adaptability in dynamic conversations. PolyAI specializes in AI-driven voice assistants designed to overcome these challenges by maintaining natural, human-like conversations, providing accurate and contextual responses, and enabling scalability. Their AI agents handle real-world conversational complexities, understanding diverse accents, managing interruptions, and adapting to context shifts.
- Latency and speech recognition errors: Minimizing delays and improving the accuracy of understanding spoken language.
- Lack of contextual awareness: Enabling AI to understand and utilize historical context for complex queries.
- Limited conversational flexibility: Creating AI agents that can adapt to dynamic and natural conversations, rather than relying on rigid scripts.
- AI Hallucinations: The Growing Problem for LLMs
- The Future of Voice AI in Customer Service
- Understanding Retrieval-Augmented Generation (RAG)
PolyAI can resolve between 50% and 75% of inbound calls entirely autonomously.At its core, PolyAI’s technology blends spoken language understanding, speech synthesis, and intelligent dialogue management. By combining retrieval-based and generative AI models, the system delivers fast, accurate, and natural-sounding responses tailored to each caller’s needs. Integration is straightforward: PolyAI’s platform works out of the box with Salesforce, Twilio, and Amazon Connect, and agents can be deployed in under six weeks without replacing existing systems. Designed for enterprise environments, PolyAI’s voice agents meet high standards of security and compliance and support more than a dozen languages. To see this technology in action, watch this demo video.
Frequently Asked Questions (FAQ)
What are AI hallucinations?
AI hallucinations occur when an AI model, particularly a generative language model, produces output that is factually incorrect, nonsensical, or not grounded in the provided data or its training. This can manifest as fabricated information, false claims, or nonsensical statements.How does PolyAI prevent AI hallucinations in its voice AI agents?
PolyAI uses a technique called retrieval-augmented generation (RAG). This process allows their AI agents to cross-reference the output of generative models with a verified knowledge base. This ensures that the responses provided are factual, relevant, and within established boundaries, thereby preventing hallucinations.What are the main challenges PolyAI addresses in voice AI for customer experience?
PolyAI focuses on overcoming key challenges in voice AI for customer experience, including:What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is a method used in AI that enhances generative models by providing them with access to external, often curated, knowledge bases. When an AI model generates a response, RAG first retrieves relevant information from this knowledge base and then uses that information to inform and ground the generated output, making it more accurate and factual.How quickly can PolyAI agents be deployed?
PolyAI agents can typically be deployed in under six weeks, making the implementation process relatively fast for enterprises.Crypto Market AI's Take
The challenge of AI hallucinations is a critical one, especially in customer-facing applications where trust and accuracy are paramount. PolyAI's approach, leveraging retrieval-augmented generation (RAG), is a robust strategy for grounding AI responses in verifiable data. This mirrors the importance of reliable data sources and factual accuracy in the financial and cryptocurrency markets. At AI Crypto Market, we understand the necessity of providing users with accurate, up-to-the-minute market intelligence and trading insights. Our platform utilizes advanced AI agents for sophisticated market analysis and prediction, aiming to mitigate risks associated with misinformation and ensure users make informed decisions. For those interested in the intersection of AI and finance, exploring our insights into AI-driven crypto trading strategies can offer further perspective on how AI is reshaping the investment landscape.More to Read:
Originally published at CX Today on August 6, 2025.