The problem we met

Every day businesses lose time, money and customer trust because their teams are stuck answering the same calls. Checking orders, confirming appointments and handling simple questions overloads call centers and keeps them from focusing on real problems and building strong customer relationships.

This drives up costs, hurts service quality and burns out staff. In areas like e-commerce, healthcare and logistics where speed matters most, it hits profits directly.

People want fast and reliable support any time. Traditional call centers can’t keep up. It’s time to rethink voice communication.

Most so-called “AI” call center bots that have been around for years don’t really solve this. They just follow strict scripts and fail as soon as someone says something different. That frustrates people and often makes things worse.

Our team looked for a better way and found it. We built a conversational AI that can keep a natural chat, understand what callers mean even in complex sentences, and respond smartly. This helps our clients stop wasting money on old solutions that don’t work and start giving customers the real support they expect.

The Moravio solution is an AI voice agent

A voice assistant answers calls right away. No waiting, no music, no transfers. It listens, understands what the caller needs and replies in a clear, natural voice. It pulls information from your own data so answers are fast and accurate. This means customers get help any time without needing a human on the line. It’s a smart and simple way to handle calls.

And because it’s not a typical old-school “AI bot,” it doesn’t rely on rigid scripts. It actually understands and can have a conversation. This is the kind of advanced solution our experts deliver — helping you move beyond outdated bots and win customer trust with truly smart automation.

Tech stack we used for this solution

This solution uses modern tools to handle real-time voice conversations with customers. Here’s what each part does.

• Twilio (Programmable Voice)
  Handles incoming and outgoing calls. Turns what the caller says into text in real time so the system can process it.
• Conversational AI Layer (Flexible LLM Integration)
  This is the core of the system. It understands what the caller means, finds the right information and gives an answer. It can use any top language model like ChatGPT, Claude or Gemini, based on what the project needs. We can also connect it to your documents or FAQs so it gives more accurate answers.
• ElevenLabs
  Quickly turns text into speech that sounds natural and human. After the AI creates a response, ElevenLabs makes sure it’s spoken out loud right away.

Together, these tools create an AI voice agent capable of understanding callers, thinking in real time, and responding with clarity and personality — all without a human in the loop.

A smart voice agent architecture

During the proof of concept, our Moravio team explored three ways to build this voice agent. Each option had its own benefits and trade-offs:

1. Conversational AI Integration via ElevenLabs

This was the fastest way to get started. We connected Twilio directly with ElevenLabs Conversational AI. Most of the setup happens in their dashboards, so you can process calls in real time without writing backend code. You still need some technical skill to link services, manage questions and set fallback answers. This gives the lowest latency and smoothest user experience, but you’re limited to what ElevenLabs offers out of the box.

2. Twilio Webhook + Custom REST API

In this approach, Twilio is configured to send incoming call data via Webhook to a custom-built REST API. The server processes the caller’s speech input synchronously: transcribes it, generates a response via a selected LLM, converts it to speech (TTS), and returns audio back to Twilio.
This gives our developers full control over the logic, data and how it connects to the client’s systems. It’s a bit slower than direct integrations but much more flexible.

3. Twilio WebSocket + Streaming Pipeline

The most advanced and flexible architecture: Twilio streams audio data in real time via WebSocket to a backend service. Here, the audio is transcribed using a custom or third-party Speech-to-Text (STT) engine, processed with a real-time LLM (e.g., ChatGPT), and synthesized back into speech using a streaming TTS service.
While this approach unlocks near-total control and extensibility, it introduces technical complexity, especially around streaming TTS, where latency and audio buffering become major challenges.

Key challenges we faced

While building the proof of concept, several technical and practical challenges surfaced, especially around real-time performance and conversational accuracy:

• Real-Time Speech-to-Text (STT) Latency
  Achieving low-latency transcription is critical for a natural voice interaction. Even small delays in speech recognition create awkward pauses that break the conversational flow. This is especially noticeable in streaming scenarios where timing is everything.
• AI Limitations with Complex Queries
  Although modern language models are incredibly capable, they still struggle with vague, ambiguous, or overly complex customer questions, especially when those queries require deep business-specific context or real-time data. Without proper RAG (Retrieval-Augmented Generation) integration, answers can become generic or imprecise.
• Speech Clarity and External Factors
  Variability in user speech, such as strong accents, background noise, or unclear pronunciation can significantly affect transcription quality and downstream understanding. This challenge becomes even more apparent in mobile environments or industries with diverse user bases.

What worked best

There are several multiple architectural paths – from fully managed integrations to fully custom streaming pipelines. Two approaches stood out as the most effective, each excelling in different types of voice interactions:

• Twilio + ElevenLabs Conversational AI
  For real-time, natural-feeling conversations, the direct integration between Twilio and ElevenLabs delivered the smoothest experience. It offered the lowest latency, minimal setup complexity (once properly configured), and highly realistic voice responses. This made it ideal for use cases where conversational flow and immediacy are critical, such as appointment scheduling or basic customer service.
• Twilio Webhook + Custom Backend
  For FAQ-style interactions or knowledge-based questions, the webhook-based architecture provided greater control and flexibility. It allowed us to easily integrate custom logic, connect to external systems or databases, and implement RAG-based enhancements. While slightly slower than the direct conversational setup, it was more scalable and better suited for content-heavy or compliance-sensitive use cases.

Real Use Case from Moravio

Tenant Support Line (AI Voice Agent)

Use Case: An automated phone interaction with a tenant seeking information about housing benefit support.

Customer calls the support line:
AI Agent: Hello, welcome to our customer service line. How can I help you today?

Customer: I’d like to ask how I can apply for a housing benefit.

AI Agent: I understand. Housing benefits are managed by the local government office. You’ll need a rent confirmation document from us, which we’re happy to provide. Would you like me to send you a link where you can easily request it online?

Customer: Yes, that would be great.

AI Agent: Perfect. I’ve just sent the link via SMS to the number you’re calling from.
— SMS sent —
Do you have any other questions regarding the benefit?

Customer: Do I need to report the benefit to you?

AI Agent: No, that’s not necessary. The housing benefit is managed directly between you and the government office. We only provide the rental confirmation document.

Customer: Alright, thank you.

AI Agent: You’re very welcome. Have a great day!

This type of call would typically take 3–5 minutes of a human agent’s time and often involves repeating the same information. Automating it:

• saves time for both customers and staff,
• reduces workload on the support team,
• and ensures consistent, professional responses every time.

Thanks to the AI agent’s ability to handle follow-up questions and guide the conversation naturally, the entire process from question to action is resolved without human intervention.

Why this matters for your business

Adding an AI voice agent brings real value from day one, especially if you get lots of calls and repeat questions.

• Lower support costs
  Handling common calls with AI means you need fewer people for routine work. This cuts staffing and training costs and lets your team focus on the tough cases.
• 24/7 phone coverage without extra cost
  Unlike traditional support teams, the AI agent never sleeps. It works all the time, answers calls at night, on weekends and holidays, so customers always reach someone.
• Scales with your business
  As your call volume grows, the AI scales too. No hiring bottlenecks, no burnout, no queues. It’s an always-on support layer that grows with your business.
• Consistent customer experience
  Every customer receives the same accurate, friendly, and professional response - regardless of time, language, or location. This removes human inconsistency and builds trust in your brand.

Why build your voice agent with Moravio

We don’t just build tech. We focus on real solutions that solve real problems. 

Our team takes care of the whole process. From designing the right architecture and picking the best AI models to setting up voice systems and making sure everything connects to your existing tools. We look at how your business works and what matters most to you. Then we build something that fits, scales and actually gets used by your team and customers.

With Moravio, you get a partner who thinks beyond the code. We keep everything running smoothly so you can focus on your business, knowing the tech side is covered and brings real value.

Ready to explore how this could work for you? Let’s talk.