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:

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.

The core components of conversational AI architecture

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.

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.

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.

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.

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.

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.

Listening

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

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.

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.

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.

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.

Conversational AI architecture FAQs

What is conversational AI architecture?

Conversational AI architecture is the framework or structure that defines how AI systems interact with users through natural language. It includes the components, data flow, and processes that allow an AI system to understand, respond, and learn from conversations.