AI Voice Agent to Answer Business Related Queries

Our Canada-based client hit a wall that many growing companies eventually face: they simply couldn’t keep up with customer calls. It’s a tough spot to be in when you want to provide high-quality, immediate answers but don’t have the staff to cover every hour of the day. We stepped in to change that by building a voice agent that handles business queries in real-time. This case study breaks down how we used low-latency technology to stop missed opportunities and give businesses a way to scale without the massive overhead of a traditional call center.

The Challenge

Business owners across almost every sector are drowning in customer inquiries. It’s an always-on world, but human teams have to sleep. Our client in Canada saw specific pain points that were hurting their bottom line:

  • Calls coming in after hours were often missed, which meant potential customers just gave up and went elsewhere.
  • Small and mid-sized companies didn’t have the budget to staff a full-time call center, making it hard to compete with larger players.
  • Most automated systems they’d tried sounded like robots and couldn’t grasp actual business context, which frustrated callers.
  • Natural conversations weren’t happening because of the lag and slow processing speeds found in older systems.
  • Every missed call was a hit to their brand reputation. If you don’t answer, a customer may not trust the service.

Our Solution

We didn’t just want a bot; we wanted a system that felt like talking to a person. To do that, we built an orchestration layer designed for speed. We used a mix of tools to make sure the agent could think and speak almost instantly.

  • Modern Frontend: We used js to create a dashboard where owners can check call logs and adjust how their agent behaves.
  • High-Performance Backend: Python FastAPI handles the logic, making sure data moves fast across the whole system.
  • Cloud Infrastructure: We hosted everything on Google Cloud Platform (GCP) to keep the system reliable and available.
  • Data Management: Supabase was our choice for keeping business inquiries and user databases secure.
  • Voice Technology: We brought in Livekit for the agent settings and Twilio to handle the actual phone lines.
  • Intelligent Processing: The “brain” uses Vertex AI, OpenAI Realtime, and Gemini native audio to understand what a caller actually needs.
  • Latency Reduction: To remove the awkward pause, we used Groq for fast hardware processing and ElevenLabs for voices that sound truly human.
  • Integrated Booking: We even built in a booking system so callers can grab an appointment right then and there.

Implementation

How do you make a machine sound natural? We focused on building a resilient environment that could handle complex logic without failing. Our team moved through the rollout in specific stages.

Architecture Mapping

To begin, we had to get the plumbing right. We set up a direct link between the language models and the phone providers. This helps stop “audio jitter,” which is the stutter often heard on bad internet calls.

Voice Customization

Nobody wants to talk to a generic computer. We used a library of high-quality voices from ElevenLabs so the client could pick a persona that fit their brand. Does your business need to sound professional or friendly? We let the client decide.

Knowledge Integration

We used Retrieval-Augmented Generation (RAG) to teach the agent. By connecting it to the client’s actual business documents, we made sure it gave accurate facts rather than just guessing.

Interrupt Handling

Have you ever tried to talk over a recorded message and it just keeps playing? We fixed that. We built “smart interruption” logic so the agent stops talking the moment the human caller speaks. It makes the whole thing feel like a real back-and-forth.

Scalable Deployment

We used GCP so the system can grow on the fly. If there’s a sudden spike in calls during a busy period, the backend adds more power automatically. You don’t have to worry about the system crashing when you’re busy.

UI Dashboard Development

We created a central hub for the business owners. They can log in anytime to see who called, read transcripts of the chats, and keep an eye on how the AI is performing. It’s all about transparency.

Testing and Security

We ran many tests to make sure the delay was so small a human wouldn’t notice it. We also baked in strong encryption at every level. Your customer data is private, and we kept it that way.

Results & Impact

By automating their inquiry process, our Canadian client cleared a major hurdle. They aren’t just saving time; they’re capturing revenue that used to slip through the cracks.

  • The agent is on the job around the clock, so no caller is ever left hanging.
  • Small businesses now have access to the kind of tech usually reserved for large corporations.
  • The automated booking system has freed up human staff to do the work that actually requires a person’s touch.

Callers get their answers fast, and they don’t have to deal with long wait times.

Key Takeaways

  • If you want a natural conversation, you have to prioritize tools that reduce lag.
  • Use RAG to give your agent the right facts so it speaks with authority.
  • Always keep a dashboard for human oversight so you know exactly what’s happening.

Ready to scale your customer service without the high cost of human call centers? Contact us today to deploy your AI voice agent.