ResillO: Building an Offline AI Triage Engine for Disaster Response

Ekansh Patil — Mon, 05 Oct 2026 06:51:55 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

Resilio is an offline-first, decentralized field notes triage engine designed for disaster relief workers, emergency responders, and operators working in connectivity-dead zones.

When power grids fail or cellular networks go down during extreme weather or infrastructure collapse, cloud-hosted AI models like GPT-4 or Claude become completely unreachable. Resilio solves this single point of failure by running local open-weight AI (Gemma 2B via Ollama) directly on edge hardware.

It ingests raw, unstructured voice logs and text reports (e.g., "Bridge near Sector 4 washed out, two medical teams stranded, need clean water immediately"), parses them in real time using local inference, extracts structured JSON triage telemetry (Priority, Category, Location, Actionable Needs), and indexes them into MongoDB Atlas with an automatic in-memory offline fallback.

Who I Built It For

I built Resilio for a friend working in field logistics and emergency response management. During major storm deployments, their primary bottleneck isn't gathering information—it's processing chaotic, raw reports when cloud APIs and internet access drop to zero. They needed a zero-latency, private, and 100% offline-capable companion that turns messy text logs into actionable emergency dashboards on a local laptop without sending a single byte to an external server.

Demo

Live Deployment: Resilio Web App

Code

coderForLife-A1 / ResillO

Offline-first disaster triage powered by edge AI.

🛡️ ResillO: Offline Emergency Notes Triage

An offline-first tool for field operators in connectivity-dead zones. It turns messy field notes into structured triage data (priority, category, location, summary, needs) using a local open-weight model (Gemma 2B via Ollama), so nothing leaves the device. Reports are stored in MongoDB Atlas, or in memory when the database is unreachable.

Quickstart (about 5 minutes)

1. Install and start the local model

Install Ollama, then:

ollama pull gemma:2b      # one-time download (~1.7 GB)
ollama run gemma:2b       # starts/loads the model; type /bye to exit the chat

Ollama serves its API at http://localhost:11434. If it isn't running, start it with ollama serve.

2. Install dependencies

python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate
pip install -r requirements.txt

3. (Optional) Connect MongoDB Atlas

export MONGO_URI="mongodb+srv://<user>:<password>@<cluster>.mongodb.net/?retryWrites=true&w=majority"
# Windows PowerShell: $env:MONGO_URI="..."

Data goes to database field_notes, collection triaged_reports If …

View on GitHub

How I Built It

Local Inference Engine: Ollama running locally on edge hardware to achieve zero-latency, offline execution without cloud connectivity.
Open-Weight Model: Gemma 2B, selected for its low memory footprint and high efficiency in extracting structured JSON telemetry (Priority, Category, Location, Actionable Needs) from messy text logs.
Architecture Design: Resilio is built entirely around local execution—Ollama acts as the central intelligence engine powering the FastAPI triage pipeline, storing structured telemetry in MongoDB Atlas with an automatic in-memory offline queue.

Why Does Open Innovation Matter?

In mission-critical infrastructure and emergency field operations, relying on proprietary, closed-source cloud APIs is a structural vulnerability. Resilio was explicitly built around open-weight models and open-source tooling because closed APIs fail completely in low-resource and disaster environments.

Prize Categories

Primary Track:

Build for a Friend: Built specifically for a friend in emergency field logistics who faces complete cloud outages in disaster-hit zones.

Partner Categories (Opt-In):

Best Use of Gemma ($200): Resilio is driven by Gemma 2B running locally via Ollama to execute zero-latency, structured JSON triage and emergency status extraction.
Best Use of MongoDB Atlas ($100): Uses MongoDB Atlas as the central datastore for multi-operator field report sync, paired with an automatic in-memory queue to gracefully handle offline network drops.

DEV Community: Ekansh Patil