How do you actually build a voice AI agent?

Where do you even start?

It all starts with mapping conversations. Before writing a single line of code, list out the most common questions, issues, and requests your customers raise on calls. These become the “call flows” your AI agent needs to handle. The goal is to identify intent — what customers actually want from the call — and create logical paths for the AI to follow. Think of it like scripting a dialogue for a staff member: you’re giving the AI context, tone, and direction. This early groundwork shapes how natural the agent feels once it’s live.

What’s the tech stack behind it?

● Speech-to-text: Converts spoken words into text for processing.
● Language model: Understands meaning, intent, and decides what to say back.
● Text-to-speech: Turns responses into a realistic, natural-sounding voice.
● Telephony integration: Connects the AI to your business phone system for inbound and outbound calls.
● CRM/booking integration: Stores caller data, updates records, and triggers workflows automatically.

Each layer plays a vital role. Together, they form what’s called the “voice orchestration layer.” When tuned properly, this stack gives you a responsive, human-like experience that runs smoothly across any device or platform.

How do you train the system?

You train a voice AI much like you’d train a new team member. Feed it with sample conversations, real FAQs, and detailed scenarios. Utilise the production data extracted from how your staff naturally handle calls. The more real-world data it hears, the better it learns context and nuance. Involving your team early helps, as they know the tone and language that suits your customers best. Training isn’t a one-off exercise either; it’s an ongoing process where feedback and new examples improve accuracy over time.

What about testing?

Testing is where theory meets reality. Don’t stop at five calls — run dozens across a wide range of situations. Include everything from clear speakers to muffled background noise, fast talkers, and hesitant callers. Throw in slang, interruptions, and tricky questions. Real callers rarely follow a perfect script, so your AI shouldn’t expect one. Record, review, and refine. Proper testing reveals what sounds clunky or confusing before customers ever experience it. It’s the difference between a decent agent and one that truly feels human.

How long does it take?

Timelines depend on complexity. Basic agents handling simple tasks like booking or answering FAQs can be up and running in a few weeks. More advanced builds — with deep CRM links, data routing, or multiple voice personas — might take a couple of months. The key is not to rush. Investing time upfront ensures smoother performance later. Once the framework’s in place, scaling is fast and efficient. The payoff is big: one system that can handle thousands of calls at once without adding staff or extra overhead.