How I Built Memory Concierge: An AI Hotel Concierge That Remembers Guests

I built Memory Concierge, an AI-powered hotel concierge prototype that remembers returning guests and helps hotel staff personalize a stay before the guest even asks.

The project is built around a fictional luxury hotel called The Aurelian Vale.

The idea is simple:

What if hotel service could feel proactive instead of repetitive?

Instead of making guests repeat their preferences every time they return, Memory Concierge helps staff remember, prepare, and adapt the guest experience in real time.

Live Demo | GitHub Repo

Memory Concierge dashboard for The Aurelian Vale.

Why I Built This
What Memory Concierge Does
Tech Stack
How I Used AI
How The App Works
Using NVIDIA NIM
Why Structured JSON Matters
Real-Time Adaptation
Adding Voice With ElevenLabs
What I Learned
What I Would Build Next

Why I Built This

I wanted to build an AI project that felt like a real workflow, not just another chatbot.

Hotels already collect a lot of useful guest information:

Room temperature preferences
Pillow type
Dietary restrictions
Favorite drinks
Morning routines
Meeting schedules
Past celebrations
Arrival details

But that information is often scattered or underused.

A returning guest may still need to repeat the same preferences every time they arrive.

I wanted to explore a different experience:

The hotel already remembers you, and the concierge quietly adapts before you ask.

That became the core idea behind Memory Concierge.

What Memory Concierge Does

Memory Concierge is a staff-facing dashboard.

A staff member can select an arriving guest, and the app generates:

A personalized welcome message
Room preparation details
Dinner or arrival adjustments
A tomorrow morning brief
A memory-based concierge note
Real-time adaptation for events like delayed flights
Optional voice playback for the welcome message

The main demo guest is Sarah Chen, a returning guest whose flight is delayed by two hours.

The concierge already knows Sarah prefers:

A high-floor city-view room
68°F room temperature
Firm pillows
Burgundy Pinot Noir
Sparkling water
A yoga mat
A morning gym routine

When her delayed flight is selected, the app generates an updated stay plan and a calm guest-facing message.

That is the main product idea: proactive hospitality powered by memory.

Tech Stack

I built Memory Concierge with:

Next.js 14 App Router
TypeScript
Tailwind CSS
Framer Motion
Lucide React
NVIDIA NIM
Llama 3.1 70B Instruct
ElevenLabs text-to-speech
Local JSON guest data
Codex as a development partner

The project is intentionally lightweight. There is no database and no authentication in the prototype. Guest memory is stored locally in data/guests.json so the demo stays simple and reliable.

How I Used AI

AI was used in two different ways.

First, I used Codex while building the project. It helped me scaffold the app, debug TypeScript issues, improve the README, polish the UI, and document the project.

Second, the app itself uses AI at runtime. When a guest is selected, the app sends guest context to NVIDIA's Llama model and asks it to return structured JSON for the dashboard.

So the AI is not just chatting — it is powering a workflow.

How The App Works

The project structure looks like this:

app/
  api/
    adapt/route.ts
    voice/route.ts
    welcome/route.ts
  globals.css
  layout.tsx
  page.tsx

components/
  AlertBanner.tsx
  DashboardHeader.tsx
  GuestSelector.tsx
  ItineraryCard.tsx
  MemoryTimeline.tsx
  RoomReadyCard.tsx
  TriggerPanel.tsx
  VoiceButton.tsx

data/
  guests.json

lib/
  elevenlabs.ts
  json.ts
  openai.ts

The main flow is:

A staff member selects a guest.
The frontend sends that guest profile to /api/welcome.
The API route calls the AI model.
The model returns structured JSON.
The dashboard renders the welcome, room status, dinner note, and tomorrow brief.
A staff member can trigger a real-time event.
The app calls /api/adapt.
The model returns updated actions and a guest-facing message.

Using NVIDIA NIM

I used the OpenAI JavaScript SDK, but pointed it at NVIDIA's OpenAI-compatible endpoint.

import OpenAI from "openai";

export const NVIDIA_MODEL = "meta/llama-3.1-70b-instruct";

export const openai = new OpenAI({
  apiKey: process.env.NVIDIA_API_KEY ?? "missing-nvidia-api-key",
  baseURL: "https://integrate.api.nvidia.com/v1"
});

The model used is:

meta/llama-3.1-70b-instruct

I chose this because the project needs clean JSON responses that the frontend can render reliably.

Why Structured JSON Matters

One important design decision was asking the model for structured output instead of free-form text.

The /api/welcome route asks for JSON like this:

{
  "welcome_message": "A warm personalized welcome",
  "room_status": "Room preparation details",
  "dinner_note": "Dinner or arrival adjustment",
  "tomorrow_brief": "Morning schedule preview",
  "concierge_note": "Personal memory-based note"
}

This matters because each field maps to a specific part of the dashboard. The model is not just generating text — it is generating UI-ready data. That made the app feel much more reliable.

Real-Time Adaptation

The /api/adapt route powers the event buttons in the dashboard.

Example events include:

Flight delayed 2 hours
Guest went vegan
Needs quiet room
Dinner moved to 9 PM

The model returns:

{
  "alert_title": "Flight Delay Handled",
  "alert_detail": "The arrival plan has been adjusted around the delayed flight.",
  "actions_taken": [
    "Updated check-in timing",
    "Held room preparation",
    "Adjusted dinner reservation"
  ],
  "guest_message": "Your arrival has been adjusted and everything will be ready when you arrive."
}

This is what makes the project feel like an active concierge system instead of a static guest profile page.

Adding Voice With ElevenLabs

I also added optional voice playback.

When the user clicks Play Welcome, the app sends the generated welcome message to /api/voice. That route uses ElevenLabs to generate MP3 audio.

const audioStream = await elevenlabs.textToSpeech.convert(CONCIERGE_VOICE_ID, {
  text,
  model_id: "eleven_v3",
  output_format: "mp3_44100_128",
  voice_settings: {
    stability: 0.6,
    similarity_boost: 0.85,
    style: 0.2,
    use_speaker_boost: true
  }
});

I made voice generation user-triggered because it costs API credits and should only run when the user actually wants to hear the welcome message.

What I Learned

The biggest lesson was that AI products work better when the model has a clear job.

For this project, the model was not asked to be a generic assistant. It was asked to act as a luxury hotel concierge and return specific structured fields. That made the output much easier to trust and display.

I also learned that polish matters a lot in AI demos. Small details like loading states, clear cards, strong README documentation, a screenshot, and a simple story make the project easier to understand.

What I Would Build Next

A demo GIF showing the full interaction
More guest profiles
Database-backed guest memory
Authentication and staff roles
Real flight-status integration
Staff approval before sending messages
Audit logs for AI-generated actions
Multilingual concierge responses
Schema validation for AI responses
Tests for API routes and UI states

Final Thoughts

Memory Concierge helped me think about AI as a workflow layer.

The most useful AI products may not be the ones that chat the most. They may be the ones that quietly read context, return structured output, and help people act faster.

That was the goal of Memory Concierge:

Make guest memory useful at the exact moment service happens.

Try the live demo | View the source code

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