How I Built Niobe: An AI Waitress with Gemini Live and Google Cloud

Providence Ifeosame — Mon, 16 Mar 2026 13:30:29 +0000

Disclaimer: I created this blog post for the purposes of entering the Gemini Live Agent Challenge hackathon. When sharing on social media, use the hashtag #GeminiLiveAgentChallenge.

What is Niobe?

Niobe is an AI waitress for restaurants. Restaurant owners upload their menu (even as a photo), get a shareable link, and customers can talk to the waitress by voice—ask about the menu, place orders, and have a natural conversation. No app install required; everything runs in the browser.

I built it using Google’s Gemini (including the Gemini Live API for real-time voice) and Google Cloud for deployment. Here’s how I put it together.

Two places where Gemini powers the product

I use Gemini in two different ways:

1. Gemini API: turning menu images into structured data

Restaurant owners don’t have to type their menu. They upload images (photos of the menu, PDFs, etc.). The Laravel backend sends those images to the Gemini API (via the Laravel AI package with the Gemini driver). Gemini returns structured text and JSON—dishes, categories, prices—and I store that in PostgreSQL. So the “brain” of the waitress (menu + context) is partly built by Gemini from images. This is configured in Laravel with GEMINI_API_KEY and the default provider in config/ai.php:

// config/ai.php
'default' => 'gemini',
'default_for_images' => 'gemini',

'providers' => [
    'gemini' => [
        'driver' => 'gemini',
        'key' => env('GEMINI_API_KEY'),
    ],
    // ...
],

Menu extraction then calls Gemini with a prompt and the uploaded image attachments; the response is parsed as JSON and saved to the database.

2. Gemini Live API: the voice conversation

The real-time voice experience is powered by the Gemini Live API. My Go voice agent doesn’t do speech-to-text or text-to-speech itself; it acts as a proxy between the browser and Gemini Live:

The customer opens the “Talk” page and connects via WebSocket to the Go service (/live?niobe=<slug>).
The Go agent loads the waitress and menu from the same PostgreSQL database Laravel uses, builds a system instruction and tool definitions, and opens a Gemini Live session using the Google GenAI SDK.
Audio flows both ways: browser ↔ Go agent ↔ Gemini Live. The model speaks and listens in real time, with natural turn-taking and interrupt handling.

When the model decides to take an action (e.g. “place order”), it sends a tool call to the agent. The Go service runs LocalNiobeTools: it writes to the database (e.g. waitress_action_logs), can send email or fire webhooks, and returns the result back to Gemini. The model then confirms to the user in voice. So: Gemini Live = voice + reasoning; Go + PostgreSQL = tools and persistence.

The Go agent connects to Gemini Live with the Google GenAI SDK and wires up system instruction, tools, and audio config:

// agent/live/google.go (simplified)
client, _ := genai.NewClient(ctx, &genai.ClientConfig{
    APIKey:      cfg.GetAPIKey(),
    HTTPOptions: httpOpts,
})
model := "gemini-2.5-flash-native-audio-preview-12-2025"

connectConfig := &genai.LiveConnectConfig{
    ResponseModalities: []genai.Modality{genai.ModalityAudio},
    SpeechConfig: &genai.SpeechConfig{
        VoiceConfig: &genai.VoiceConfig{
            PrebuiltVoiceConfig: &genai.PrebuiltVoiceConfig{VoiceName: "Aoede"},
        },
    },
    SystemInstruction: &genai.Content{
        Parts: []*genai.Part{genai.NewPartFromText(systemInstruction)},
        Role:  genai.RoleUser,
    },
    Tools: tools,
}
sess, _ := client.Live.Connect(ctx, model, connectConfig)

From there, a proxy bridges the browser WebSocket and this session so audio and tool calls flow both ways.

Why Google Cloud?

I run the app on Google Cloud so that the Laravel app and the Go agent can share one Cloud SQL (PostgreSQL) instance. The agent is deployed as a container (e.g. Cloud Run), and the web app is deployed via Terraform and Cloud Build in the deploy/ folder. Same VPC and database mean low latency and a single source of truth for menus, waitresses, and action logs. Configuration is via environment variables—no secrets in code:

# Agent (Go) – example .env
GEMINI_API_KEY=your_key
DATABASE_URL=postgres://user:password@/niobe?host=/cloudsql/PROJECT:REGION:INSTANCE
PORT=8080
APP_URL=https://your-laravel-app.run.app

# Optional: use Vertex AI instead of Gemini API
# GOOGLE_GENAI_USE_VERTEXAI=true

Architecture in one picture

Browser → Laravel (HTTPS) for dashboard and menu upload; Laravel uses Gemini API for menu extraction and writes to PostgreSQL.
Browser → Go agent (WebSocket) for voice; the agent reads from PostgreSQL, talks to Gemini Live API, and runs tools (DB, email, webhooks) in-process.

So: one database, two Gemini touchpoints (Gemini API for menus, Gemini Live for voice), and Google Cloud to host and connect it all.

What I’d do next

I’d add more tool types (e.g. table reservation, kitchen display), support for more languages, and tighter integration with POS systems. The current stack—Laravel + Vue/Inertia for the app, Go for the voice proxy, Gemini for vision and live voice, and Google Cloud for deployment—gives me a clear path to scale.

If you want to see the code or run it yourself, check out the repo and the architecture doc for diagrams and data flows.

This post was created for the purposes of entering the Gemini Live Agent Challenge hackathon. #GeminiLiveAgentChallenge

DEV Community: Providence Ifeosame