How PolyAI helps enterprises deploy production-grade voice AI agents faster
Enterprise interest in AI agents is booming, thanks to large language models (LLMs) that promise to simplify complex tasks. But as teams move from promise to production, many are discovering that building with AI requires a new kind of thinking – one that’s powerful, but fundamentally different from traditional software development.
Navigating a new development model
Interactive Voice Response (IVR) solutions once frustrated both callers and developers with repeated “sorry, I didn’t understand” messages. In traditional software development, you wrote code to send an API request, parse a defined response and handle known errors in predictable ways. Testing was clear-cut.
Today, with LLM-based agents, development works differently. Developers guide models to say “I don’t know” when appropriate – an essential part of building trustworthy experiences. This requires prompting the model to reason probabilistically, rather than following fixed rules.
The shift means guiding the model to associate customer intent with the correct API call and the necessary parameters to execute the API call successfully – a more flexible, less deterministic approach.
Designing for consistency and clarity
Even small changes in phrasing or context can lead to variations in the model’s interpretation. As a result, evaluating performance is no longer about binary success. Instead, it’s about assessing how reliably the model maps natural language to structured API calls across a wide variety of inputs, edge cases and unseen user behaviour.
With new metrics and processes required, bridging the gap from proof-of-concept to production-grade application becomes a significant business challenge. Indeed, Gartner predicts that at least 30% of generative AI projects will be abandoned after proof-of-concept by the end of 2025. As AI technology advances, aligning enterprise expectations, workflows and development methods will be critical to delivering long-term value.
Voice AI’s transformative promise
Even with these new dynamics, the opportunity for voice AI agents is vast. Contact centres – complex, high-volume environments – may be transformed by agentic solutions that can scale, support human agents or enable autonomous customer experiences.
For the first time, voice becomes a truly intelligent interface. AI agents can interact conversationally, adapt in real time, and generate new insights into customer preferences and the overall customer journey at a previously unimaginable scale.
This isn’t just a new channel – it’s a smarter way to support one of the enterprise’s most personal customer touchpoints.
Building for production: PolyAI’s approach
PolyAI has worked with LLMs since 2017, partnering with leading enterprises like Pacific Gas & Electric, Caesars Entertainment and Unicredit to deploy voice agents that manage millions of conversations with fluency and enterprise-grade reliability.
That experience shaped PolyAI Agent Studio – our enterprise platform designed to help teams build, manage and continuously improve production-grade voice agents.
Powering our platform is a tightly integrated suite of proprietary models: Owl for speech recognition and Raven for reasoning. These models give us something off-the-shelf systems can’t – fine-grained control, deep observability and continuous learning from real-world conversations.
With reinforcement fine-tuning, our agents become more reliable over time, improving how they respond, manage uncertainty and support seamless customer conversations.
Our context-orchestration framework lets developers connect from CRMs, telephony, APIs or user history with precision, allowing personalised, brand-consistent interactions at scale while limiting the risk of hallucinations.
At the heart of our platform is fluency: enabling AI agents to respond with relevance, continuity and clarity.
Deploying with Microsoft Azure
By extending Agent Studio to Microsoft Azure, enterprises gain more control over how they deploy PolyAI agents, whether for compliance, data residency or closer integration with their Microsoft ecosystem.
We’re also building integrations with Dynamics 365 Contact Center and Microsoft Teams, enabling faster deployment of voice AI agents into high-value customer service workflows.
Find out more
Visit the Microsoft Azure Marketplace for more about PolyAI’s solution
Read MIT’s Technology Review Insights Report on Customizing Generative AI
About the author
Michael Chen is the VP of Strategic Alliances and Corporate Development at PolyAI. He leads a team focused on expanding PolyAI’s collaborative relationships with cloud providers, technology partners and global systems integrators. Michael has been on the frontier of the LLM and generative AI revolution since 2020, bringing expertise and perspectives across corporate strategy, customer experience, product marketing and business transformation.
