Beyond AI Journaling: Building a Secure Scientific Research Companion with Gemini and Cloud Run

Description and use

AI Scientific Journal consolidates scattered scientific observations into a unified platform, leveraging Gemini for analysis and Google Cloud Run for secure hosting.

Problem

Scientific observations are often scattered across notes, photos, measurements, and questions, making it hard to organize, analyze, and retrieve insights efficiently.

Outcome

The AI Scientific Journal unifies scattered research data, provides AI‑driven insights, and offers secure, scalable hosting, improving organization and analysis for researchers.

Workflow

• 1. Capture observations (notes, photos, measurements) via the web UI.
• 2. Store entries in Firebase Firestore with metadata.
• 3. Use Gemini API to generate analysis and insights for each entry.
• 4. Enable AI chat to continue ideas and ask follow‑up questions.
• 5. Implement retrieval‑based search to surface relevant past entries.
• 6. Deploy the Dockerized application to Google Cloud Run for scalable, secure hosting.

Evidence

I built AI Scientific Journal to bring them together in one place.

Source

I gained practical experience building a secure, cloud-deployed AI application with Firebase, Firestore, Gemini, retrieval-based search, Docker, and Google Cloud Run.

Source