Building Sanctuary AI: A Privacy-First AI Journal with Gemini and Google Cloud Run

A developer built Sanctuary AI, a privacy-first AI journaling application using Google Cloud Run, Firebase Authentication, Cloud Firestore, and Google Gemini. The app allows users to write or voice-record reflections, analyze them with Gemini, and engage with AI personas for Socratic questioning and cognitive reframing, with user data isolated via user-scoped Firestore collections and security rules.

Personal journaling is useful, but it can be difficult to turn a collection of thoughts into meaningful insights or actionable goals. I wanted to experiment with a different approach: what if an AI could act as a reflection companion while keeping each user's journal data isolated and securely managed? That idea became Sanctuary AI, a cloud-native personal reflection and journaling application built with Google Cloud Run, Firebase Authentication, Cloud Firestore, and Google Gemini. The core workflow is simple: Reflect → Understand → Act → Review What does Sanctuary AI do? Sanctuary AI allows an authenticated user to: ✍️ Write and save personal reflections 🎙️ Create reflections using voice 🧠 Analyze reflections with Gemini 💭 Explore thoughts using Socratic questions 🔄 Get alternative perspectives and cognitive reframing 🤝 Chat with different AI Companion personas 🔎 Ask questions about previous journal entries 🎯 Convert insights into actionable goals ✅ Track goals and tasks 📊 Review recurring themes and insights 📦 Export personal data 🗑️ Delete their account and associated data The idea is not to replace personal reflection with AI. Instead, AI is used as a tool to help the user think more deeply about their own experiences. Architecture The application uses Google Cloud services for authentication, storage, backend processing, and AI. Sanctuary AI │ ▼ ┌───────────────┐ │ Web Client │ └───────┬───────┘ │ ▼ Firebase Auth │ ID Token │ ▼ ┌───────────────┐ │ Cloud Run │ │ Backend │ └───────┬───────┘ │ ┌──────────┴──────────┐ ▼ ▼ Cloud Firestore Gemini API User Data AI │ ▼ User-scoped data Cloud Run handles the backend logic while Firestore stores application data. Gemini provides the AI capabilities. Firebase Authentication provides the identity layer. Keeping User Data Isolated Because this application deals with personal reflections, data isolation was one of the most important design considerations. User data is organized using user-scoped Firestore collections: /users/{userId}/journals /users/{userId}/goals /users/{userId}/insights /users/{userId}/conversations /users/{userId}/messages Firestore security rules enforce ownership: request.auth.uid == userId The backend also validates the Firebase ID token and derives the authenticated user's identity from the token instead of trusting a user-provided user ID. This creates a clear boundary between users. Note: Firestore security rules provide authorization and data isolation. They should not be confused with end-to-end encryption. Gemini-Powered Reflection One of the main challenges was deciding how Gemini should actually be useful inside a journal. Instead of creating only a general-purpose chatbot, I created focused AI workflows. A reflection can be processed to generate: Key themes Socratic questions Alternative perspectives Summaries Potential action items Goal suggestions For example, instead of simply telling the user what to do, the Socratic workflow can encourage deeper thinking by asking questions about assumptions, evidence, and possible alternatives. This keeps the user involved in the reflection process. AI Companion Sanctuary AI also includes multiple interaction styles. Socratic Guide Designed to encourage deeper thinking through questions. Compassionate Empath Designed for supportive and empathetic reflection. Execution Coach Focused more on practical next steps and execution. The purpose is to make the AI interaction more useful depending on what the user needs at that moment. Ask My Journal One of the features I particularly wanted to build was Ask My Journal. Instead of asking Gemini general questions, the user can ask questions about their own previous reflections. For example: "What topics have I been thinking about repeatedly?" "What goals have I mentioned recently?" "What patterns appear across my reflections?" "What did I write about this topic previously?" The system uses the user's journal history to help answer these questions and can reference relevant reflection dates. This turns the journal from a collection of individual entries into something that can be explored over time. Voice Journaling Sometimes writing isn't the easiest way to capture an idea. Sanctuary AI supports voice journaling using browser speech capabilities. The flow is: Speak ↓ Speech Recognition ↓ Journal Text ↓ Save Reflection ↓ AI Analysis This makes it easier to capture thoughts quickly. From Reflection to Action Another important part of the application is connecting reflection with action. An insight can be converted into a goal containing tasks, priority, and progress. Goal ├── Priority ├── Progress ├── Task 1 ├── Task 2 └── Completion Status This creates a bridge between: "I realized something" and "I am going to do something about it." Insights and Periodic Reviews The application also looks beyond individual journal entries. Over time, users can review recurring themes and insights from their reflections. The workflow becomes: Reflections ↓ AI Analysis ↓ Recurring Themes ↓ Insights ↓ Goals ↓ Periodic Review This is where the Reflect → Understand → Act → Review model becomes useful. Using Google AI Studio I used Google AI Studio while developing and refining the AI workflows. The development process looked roughly like this: Idea ↓ Prompt Design ↓ Test in AI Studio ↓ Evaluate Response ↓ Refine Instructions ↓ Integrate into Application This was especially useful for experimenting with the different AI Companion personas and reflection workflows before integrating them into the application. Handling AI Reliability AI APIs can occasionally experience latency or failures. For that reason, I designed the AI integration with resilience in mind, including fallback and timeout/circuit-breaker concepts. The basic idea is: Primary Model ↓ Failure / Timeout? ↓ Fallback Model ↓ Failure? ↓ Next Fallback This helps prevent a temporary AI service problem from making the entire application unavailable. Security and Privacy Privacy was considered throughout the application architecture. Some of the key decisions include: Firebase Authentication for user identity User-scoped Firestore data Ownership-based Firestore security rules Server-side handling of Gemini credentials Google Cloud Secret Manager for sensitive configuration Backend token validation User data export Account/data deletion There are also areas I would like to improve further, particularly around stronger encryption and minimizing sensitive information sent to external AI services. Why Cloud Run? Cloud Run provides a simple way to deploy the backend as a containerized service without managing traditional servers. For this project, it fits well because the application needs a backend that can: Authenticate requests Communicate with Firestore Communicate with Gemini Manage application logic Scale with demand Run securely behind a public HTTPS endpoint The deployment flow is essentially: Application ↓ Container ↓ Cloud Run ↓ HTTPS Service What I Learned The biggest lesson from this project was that building an AI application is not just about calling an LLM API. The surrounding engineering is equally important: Authentication Authorization Data isolation Prompt engineering Secret management AI reliability Backend architecture User experience Data ownership Gemini is one component of the application, not the entire application. The final system combines: Firebase Authentication + Cloud Firestore + Cloud Run + Gemini + Google AI Studio + Privacy & Security ↓ Sanctuary AI Try the Application 🌐 Live Demo: https://private-ai-journal-reflection.ai.studio https://private-ai-journal-reflection.ai.studio 💻 Source Code: https://github.com/KVedang/Stability-AI https://github.com/KVedang/Stability-AI The project is also part of my participation in the Accelerate AI with Cloud Run challenge. What's Next? Some improvements I would like to explore include: More advanced semantic retrieval Better long-term journal analysis Additional privacy controls Improved personalization More sophisticated insights Stronger encryption options Improved goal analytics Final Thoughts Sanctuary AI started with a simple question: Can AI help people understand their own reflections without taking the reflection away from them? The result is a platform built around: Reflect → Understand → Act → Review The goal isn't to have AI make decisions for the user. It's to provide a private space where AI can help users ask better questions, discover patterns, and turn their own reflections into meaningful actions.