Building an AI Support Agent That Speaks Nigeria 🇳🇬

by | Mar 15, 2026 | Featured News, Tech News

Introduction

Across Nigeria, millions of people still face barriers when interacting with modern technology—not because the technology does not exist, but because it often does not speak their language.

Customer support systems, AI assistants, and digital services are largely built around English, leaving many people behind—especially those who communicate primarily in Hausa, Yoruba, Igbo, or Nigerian Pidgin.

To address this challenge, I built Drisa_AI Multilingual Support Agent, a real-time AI-powered voice and chat agent that allows businesses to automatically interact with customers in multiple Nigerian languages.

Desktop View of the Drisa_AI User Interface Dashboard

The goal is simple but powerful:

Use artificial intelligence to bridge the language gap between modern technology and everyday Nigerians.

And to achieve this, I leveraged Google AI models and Google Cloud infrastructure.

The Vision: AI Support That Speaks Nigeria 🇳🇬

Drisa_AI is designed to function as a live AI support agent that can serve multiple roles for businesses:

  • Customer Care Agent
  • Sales Representative
  • Technical Support Assistant
  • Personal Assistant

The system allows customers to interact naturally via voice calls or chat, while the AI agent understands and responds in:

  • English
  • Hausa
  • Yoruba
  • Igbo
  • Nigerian Pidgin

By enabling low-latency voice conversations, the platform ensures interactions feel natural and human-like.

This approach helps make AI accessible to uneducated users, rural populations, and people with disabilities, who may find traditional text-based interfaces difficult to use.

The Technology Stack

The system combines modern web technologies with Google’s AI and cloud infrastructure to create a scalable live AI agent.

Languages

  • TypeScript (TS & TSX)
  • JavaScript

Frontend

  • React Web Framework

Backend

  • Node.js
  • Express.js

Artificial Intelligence

  • Google Gemini API
  • Gemini Live API for real-time multilingual conversations

Cloud Infrastructure

  • Google Cloud Platform
  • Google Cloud Run (Serverless deployment)

Databases

  • SQLite (Product catalog and knowledge storage)
  • Google Firestore (cloud-based data persistence)

Communication APIs

  • Twilio Voice API for phone call interactions
  • WhatsApp and Email integrations for follow-ups

System Architecture

At the core of the system is a Node.js backend running on Google Cloud Run, acting as the orchestration layer between the user, the AI model, and external services.

The architecture consists of several key components.

1️⃣ Client Interaction Layer

Users interact with the system through:

  • Phone calls (via Twilio)
  • Web-based chat interface
  • Voice-enabled browser sessions

Voice streams are transmitted using WebSockets, enabling low-latency communication between the user and the backend.

2️⃣ Backend Core

The backend server performs several important roles:

  • Session Management – handles active conversations
  • Audio Transcoding – processes incoming voice streams
  • Tool Orchestration – routes requests to AI models or databases
  • RAG Engine – retrieves knowledge from the product catalog

This orchestration layer ensures the AI agent can combine language intelligence with real business data.

3️⃣ AI Intelligence with Google Gemini

The heart of the system is powered by Google Gemini models.

Using Gemini Live API, the agent can:

  • Understand multilingual speech
  • Generate natural conversational responses
  • Process voice interactions in real time
  • Support low-latency audio conversations

Gemini enables the AI to function not just as a chatbot but as a true conversational agent capable of supporting complex interactions.

This is where Google’s AI ecosystem truly shines.

4️⃣ Knowledge and Data Layer

The system connects to databases to retrieve structured information.

Two primary databases are used:

SQLite

  • Product catalog
  • Knowledge base
  • Business information

Google Firestore

  • Follow-up requests
  • Conversation logs
  • Cloud-based persistence

This allows the AI to give accurate and contextual answers, rather than generic responses.

5️⃣ Follow-up and External Integrations

Drisa_AI also includes automation capabilities.

The system can log requests for:

  • WhatsApp follow-up
  • Email communication
  • Appointment scheduling

This ensures that when the AI cannot fully resolve a request, the conversation can be seamlessly handed over to human agents.

Deployment on Google Cloud

One of the major strengths of the project is its serverless deployment architecture.

The application is deployed using:

Google Cloud Run

Cloud Run provides:

  • Automatic scaling
  • Containerized deployment
  • High availability
  • Low operational overhead

This makes it ideal for AI applications that need to scale dynamically as call volume increases.

Deployment can also be automated using scripts, ensuring fast iteration during development.

Challenges Faced During Development

Building a real-time multilingual voice AI system came with several technical challenges.

Real-Time Audio Processing

Maintaining low latency voice streaming required careful handling of WebSockets and media streams.

Multilingual Understanding

Supporting Nigerian languages in conversational AI required extensive testing to ensure responses remained natural and accurate.

Telephony Integration

Connecting AI models with live phone calls via Twilio required proper session handling and streaming protocols.

ES Module Compatibility

Since the backend uses Node.js with TypeScript, several issues arose around ES module compatibility and dependency management.

Resolving these issues helped improve the stability and performance of the backend architecture.

Lessons Learned

This project reinforced several key insights:

  • AI must be inclusive to be impactful
  • Voice interfaces are powerful for emerging markets
  • Multilingual AI will play a major role in digital inclusion
  • Cloud infrastructure is essential for scalable AI systems

Working with Google AI models and Google Cloud services demonstrated how modern tools can accelerate innovation.

With the right architecture, it becomes possible to build real-world AI systems that can serve millions of people.

The Future of Drisa_AI

The current version already supports:

  • Real-time voice conversations
  • Multilingual support
  • Product catalog queries
  • Automated follow-up systems

Future improvements will include:

  • Expanded African language support
  • AI-powered sales automation
  • Advanced analytics for businesses
  • Deeper integration with enterprise systems

The long-term vision is to build Africa’s most accessible AI support platform.

Final Thoughts

Artificial intelligence should not be limited to people who speak a particular language or have advanced technical literacy.

With platforms like Google Gemini and Google Cloud, developers now have the tools to build systems that are truly inclusive and globally scalable.

Drisa_AI Multilingual Support Agent is one step toward that future.

AI Support That Speaks Nigeria. 🇳🇬

This article was created as part of my entry for the #GeminiLiveAgentChallenge hackathon.

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