Conversational AI architecture: Core components & proper implementation is key to scaling

We come across a lot of companies that are experimenting with DIY conversational voice assistant capabilities. However, when we get further into discussions, most of these companies have not put their voice assistant into action in a real customer support scenario.

Their proof-of-concept remains under lock and key for months, interacting only through pre-scripted queries of testers selected from within the project team.

The technology and expertise required to deploy an effective voice assistant explain why only 11% of enterprise applications reach production. While solutions may perform well in testing, scaling introduces complexities like speech recognition errors and background noise, which can cause them to deliver a poor customer experience. There’s a significant risk that what you build will not reach deployment-level quality, resulting in sunk cost and effort.

Where general-purpose conversational AI solutions fall short

There are a number of predictable risks that can hold up the initial design process:

• What happens if the voice assistant misunderstands a customer?
• What if a customer raises their voice?
• Is it ok for the voice assistant to cut off a customer mid-sentence?
• What should the warmth and tone of the voice assistant be?

If a voice assistant for customer service is built on platforms such as Google Dialogflow or Amazon Lex, those risks are more significant because the technology is general purpose. They are built to support text chatbots, smartphones, and smart speakers across a broad number of intents.

Existing DIY conversational AI platforms have not been optimized for the specific Automatic Speech Recognition (ASR) challenges of phone support, like line static, accents, or background noise. Nor have the Natural Language Understanding (NLU) models been optimized for the nature of customer service conversations: longer explanations, digressions to other topics, interruptions, and specific lexicons. As a result, achieving performance at scale has not been as simple as turning on a ‘voice’ feature.

Enterprises have limited options to manage and control these risks during live deployment. Product teams can turn features on and off or tweak training phrases here and there, but they are little more than stabs in the dark without being able to troubleshoot with the software engineers in charge of the underlying technology. When real customer calls are involved, and the stakes are high, this can understandably grind any plans for a live deployment to a halt.

The core components of conversational AI architecture

The core components of conversational AI architecture form the backbone of any effective AI-driven interaction, guiding how the system interprets, responds, and engages with users. Here are some essential elements, from the user interface to complex integrations, revealing how they work together to deliver smooth, human-like interactions.

1. User interface

The user interface (UI) is where users directly interact with the AI, either by typing or speaking. It’s the part of the system that users see or hear and can be integrated into websites, apps, social media channels, and messaging platforms.

Designing for text and voice interfaces requires different approaches. In text-based AI chatbots, users can skim or revisit messages, making it easier to share longer responses. But with voice interfaces, users rely on real-time responses and can’t skim, so answers must be short, clear, and to the point. Making these interactions smooth and natural requires dialogue designers with expertise in voice, ensuring that spoken interactions are effective and easy to follow.

2. Natural language processing (NLU)

Natural Language Understanding (NLU) is the technology that allows AI systems to make sense of natural human language, enabling meaningful interactions. It’s a key part of how conversational AI understands what users are saying.

A central concept in NLU is intent. An intent is a predefined category that tells the system what the user wants to achieve with their message. Importantly, intent doesn’t represent a user’s intention in the general sense; instead, it’s a label that triggers the next action in the conversation. For instance, when a user says, “I want to book a table,” the intent might be set to “book_table,” prompting the AI to ask for more details on the booking.

But not all intents are so direct. For example, a simple “yes” is often categorized as an “affirmation” intent, which tells the system to proceed with the current conversation flow. In short, NLU-driven intents guide the AI’s responses to ensure relevant information is given and the conversation is kept on track.

3. Dialogue management

Dialogue management is a control layer that sits on top of LLMs to enable your company to have full control over transactional processes. This component keeps track of the conversation context, remembers key details shared by the user, and determines the best next response or action.

For example, if a user says, “I’d like to book a table,” and then follows up with “for four people at 7 p.m.,” the dialogue manager keeps track of each part of this request. This way, it can ask relevant questions, confirm the details, and predict the most helpful response at each stage.

By leveraging dialogue management, conversational AI can provide responses that feel logical and personalized, guiding the conversation smoothly toward the user’s goal.

4. Natural language generation (NLG)

Natural Language Generation (NLG) is the process by which conversational AI transforms its understanding of a user’s request into a response. For interactions over the phone, NLG not only creates relevant responses that are accurate but also ensures that the tone of voice matches the brand and situation.

For example, if the AI needs to inform a user that a service is unavailable, it can use a gentle, apologetic tone, saying something like, “I’m sorry, but that service isn’t available at the moment.” Or, in sensitive cases like a bereavement, the AI can adopt a softer, more empathetic tone.

NLG’s main goal is to make the AI’s responses sound genuinely human. By crafting language that feels conversational and considerate, NLG ensures that each interaction is smooth, on-brand, and respectful of the user’s needs and emotions.

5. Integrations

Integrations connect conversational AI with other tools and platforms, enabling smooth operations and communication across systems.

For basic call routing, a Session Initiation Protocol (SIP) or Public Switch Telephone Network (PSTN) connection routes calls between the voice assistant and your team. This setup is standard for most voice assistants and can be managed by your IT team with support from the AI vendor.

For more advanced tasks like accessing customer data, processing payments, or managing bookings, the AI connects to back-end tools—such as CRMs, payment providers, and booking systems—through API integrations. These integrations allow the AI to securely access and update information in real time, ensuring it can manage a range of tasks autonomously for a seamless user experience.

How conversational AI systems are built

When it comes to deploying voice AI, enterprises must decide whether to build their own solution or buy from a provider. Some routes to deploying voice AI are more resource-intensive than others and will impact cost, quality, and the speed of deployment.

High-quality conversational experiences start with high-quality components, each requiring fine-tuning for optimal performance. To deliver a great customer experience over the phone, solutions like voice assistants must successfully listen, understand, and respond to what the customer is saying. This requires a specific tech stack to handle the nuances of natural conversation.

Listening

Accents, background noise, speech recognition errors, and named entities make it very difficult to accurately capture spoken language over the phone.

A great listening stack includes:

• Automatic speech recognition (ASR) – These systems transcribe spoken language into text that can be digested by LLMs. They are often fine-tuned for specific accents, languages, and use cases, so you may need to use several models concurrently for accurate understanding.
• Spoken Language Understanding (SLU) models – Even the best ASR will result in gaps and errors in transcriptions. SLU models recover important information from incorrect speech transcriptions, using context and customer information to infer the correct input.

Brands that want to build a voice assistant can use APIs for automatic speech recognition or build an in-house system. Building an ASR system in-house gives you more flexibility to train models on specific data and tailor them to each use case.

Reasoning

Once the speaker’s words have been transcribed, the voice assistant needs to understand the context behind the user query and how to respond in a way that continues to move the conversation toward an appropriate resolution.

A great reasoning tech stack will include the following:

• Large Language Models (LLMs) – These machine learning models can extract meaning from words and sentences and define the next steps the system should make in the context of the conversations.
• Dialogue management – A control layer that sits on top of LLMs to enable your company to have full control over transactional processes.
• Safety guardrails – A set of technical features that protect against prompt injections and other types of malicious user behavior.

Speaking

Once the voice assistant has listened and understood the caller’s intent and the appropriate response, it then must turn that response into speech.

Even with the best technology, a robotic, unnatural voice provides a subpar brand experience and discourages callers from engaging, eliminating the benefits of voice automation.

A great speaking tech stack will combine voice cloning technology with state-of-the-art synthesis and the talent of voice actors to create an experience that sounds like talking to a real person.

Deploying a voice assistant in your contact center can greatly enhance customer experience and operational efficiency. Truly scalable conversational AI requires an expert understanding of every component in this technology stack.

The right voice AI solution will help your contact center handle routine calls efficiently, allowing your agents to focus on more complex and valuable interactions. By carefully evaluating your options and aligning them with your business needs and goals, you can enhance customer satisfaction and deliver exceptional customer experiences.

PolyAI brings you the best of conversational AI

At PolyAI, we build and deploy voice assistants crafted to understand the conversations of specific customer journeys. Our experience has shown that human-level performance comes from close collaboration across all layers of the conversational AI stack, from ASR to dialogue management.

That’s why our proprietary conversational platform was built to give us complete control over augmenting ASR to reduce transcription errors. It also allows us to fine-tune our NLU model to increase accuracy in critical moments—those habitual pauses, mumbles, and clarifications—to make a conversation flow.

Optimizing dialogue management for contextual understanding

We optimize dialogue management to improve the understanding of context in each conversation, making it straightforward for customers to get what they need and drastically reducing the time needed to make changes later on. During live deployments, our dialogue designers analyze customer calls to proactively optimize performance. These make all the difference between a narrow proof-of-concept and a live deployment that can deliver real customer and business value.

A robust approach to conversational AI

It’s expensive (and difficult!) to build any, let alone all, of these capabilities in-house, and for most companies, it does not make sense. However, that should not stop companies from launching new customer experiences with conversational AI. We believe that working in a trusted partnership with access to world-class engineering and research talent offers the most robust path to value for voice assistants in customer service.

It’s critical to look for vendors that have designed their conversational platform specifically for customer service, with a proven track record in outperforming existing general-purpose conversational AI.

Discover how PolyAI can help you deliver effortless CX at scale with a conversational platform that lets your customers speak naturally, interrupt, change topics — and always have a fantastic customer experience.