Guardian — The AI Safety Companion I Built for My Friends & Family

Shivam Singh — Sun, 04 Oct 2026 19:21:09 +0000

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

What I Built

This project started with a real incident.

My friend Shubham got scammed on the day his pocket money arrived.

He decided to buy lottery tickets and purchased two tickets for ₹1,000 each. Later, when he checked his lottery number on a website, it showed that he had supposedly won ₹15 lakh.

Soon after, he received messages telling him that he had won the lottery and that he needed to pay ₹2,000 through a QR code to process the ₹15 lakh payment.

He believed it.

He paid the ₹2,000.

After that, the person who had contacted him stopped responding.

The lottery tickets had already cost him ₹2,000, and the additional payment added another ₹2,000, making the total loss ₹4,000.

That incident made me think about a problem I see much more often.

My parents, relatives, and friends regularly send me messages like:

"Is this real?"

"Should I click this link?"

"They are asking for money. Is this a scam?"

Not everyone knows how to inspect a suspicious URL, recognize social-engineering tactics, or understand whether a payment request is legitimate.

So I built Guardian.

Guardian is a WhatsApp-first AI safety companion that lets someone simply forward a suspicious message before acting on it.

It can analyze:

Text messages
Suspicious URLs
Screenshots and images
Voice notes
English, Hindi, and Hinglish

Instead of giving a complicated technical explanation, Guardian responds with three simple things:

Risk → Why → What to do

The goal isn't to replace human judgment or claim that something is "100% a scam."

The goal is much simpler:

Give people a second opinion before they click, pay, or share something sensitive.

And that is where the name comes from:

Before you trust it, ask Guardian.

Demo

So I recreated the story that inspired Guardian with my friend Shubham.

The video starts with me asking him:

"Shubham, batao tumhare saath kya hua?"

He explains how he received a message saying that he had won ₹15 lakh in a lottery, and was then asked to pay money through a QR code to process the prize.

After hearing his story, I introduce the simple solution:

Before paying, clicking, or sharing anything suspicious — forward it to Guardian.

Then, instead of just explaining how Guardian works, I actually use it in the same video.

I forward the suspicious message to Guardian and show how the entire process works from a user's perspective:

Forward → Guardian analyzes → Get a simple explanation → Decide safely

The important part is that the user doesn't need to understand AI, phishing, URLs, OCR, search APIs, or any technical details.

They just need to know how to forward a message on WhatsApp.

That's the experience I wanted to build for my parents, relatives, friends, and anyone who isn't comfortable evaluating suspicious digital messages themselves.

Video Demo

https://youtu.be/9LDguc0cTao?si=9hfbhi0uaWPZ-g9O

The demo shows:

The real problem that inspired Guardian
The lottery scam scenario
How Guardian identifies the risk
How simple the WhatsApp interaction is
How a user can get a second opinion before making a payment

Try Guardian Yourself

Don't just read about Guardian, try it.

Send a suspicious message, link, screenshot, or voice note directly to Guardian on WhatsApp:

📱 WhatsApp: +91 9236851502

👉 Try Guardian on WhatsApp

Guardian will analyze it and reply with a simple:

Risk → Why → What to do

If you've ever received a message and wondered "Is this real or a scam?" — send it to Guardian.

For testing, please use sample or non-sensitive messages. Never send real OTPs, passwords, banking details, or other confidential information.
I wanted the demo to show the problem first, not just the technology.

Code

The complete source code is open source on GitHub:

Guardian — GitHub

The repository contains the FastAPI backend, WhatsApp Web bridge, OCR pipeline, Gemma reasoning layer, SerpApi URL verification, ElevenLabs speech-to-text integration, Sentry tracing, and deployment configuration.

Architecture

WhatsApp
   ↓
Baileys WhatsApp Web Bridge
   ↓
Message normalization
   ↓
Safety signal analysis
   ↓
URL extraction + SerpApi verification
   ↓
Gemma risk reasoning
   ↓
Safe response formatting
   ↓
WhatsApp

How I Built It

Guardian is built around Gemma, an open-weight AI model, as the core reasoning layer. I use Gemma 3-27B Instruct through an OpenAI-compatible interface, which keeps the model layer replaceable and leaves a clear path to local inference later.

Gemma 3-27B Instruct analyzes the message and produces a structured risk assessment.
SerpApi provides live web evidence when a suspicious URL needs verification.
Tesseract OCR extracts text from screenshots locally.
ElevenLabs Speech-to-Text converts voice notes into text before sending them through the same Guardian pipeline.
Baileys connects Guardian to WhatsApp, allowing users to simply forward suspicious messages.
Sentry Agent Tracing Guardian's AI workflow is also instrumented with Sentry Agent Tracing so I can see where time is spent across the pipeline. For example, a single request can be traced across message normalization, safety analysis, URL verification, Gemma reasoning, and response formatting.

The backend is built with FastAPI, while the WhatsApp bridge is built with Node.js and deployed on Render.

I intentionally kept the AI layer modular. Today Guardian uses hosted Gemma through an OpenAI-compatible API, but the same architecture can be adapted to a self-hosted/local Gemma deployment without redesigning the entire application.

The goal was not to build another chatbot. It was to make AI-powered scam checking as simple as forwarding a WhatsApp message.

Why Does Open Innovation Matter?

Guardian deals with messages that can contain sensitive information, suspicious links, screenshots, and personal conversations. For this kind of product, I did not want the AI layer to be a completely closed and irreplaceable component.

Using an open-weight model like Gemma gives Guardian a much more flexible foundation. The model can be replaced, self-hosted, fine-tuned, or moved to local inference as the project evolves. The rest of the Guardian pipeline does not need to be redesigned around a single closed AI provider.

Open innovation also makes the architecture more adaptable. Today Guardian uses hosted Gemma through an OpenAI-compatible interface, but the same reasoning layer can be connected to a self-hosted Gemma deployment in the future.

For a safety-focused tool, that flexibility matters because privacy, cost, deployment environment, and model control can change over time.

Prize Categories

Guardian is being entered into the following categories:

Featured Categories

Best Use of Gemma — Gemma 3 27B Instruct is the core reasoning model used by Guardian to analyze suspicious messages and generate risk assessments.
Best Use of Render — Guardian's backend and WhatsApp bridge are deployed on Render.

Partner Categories

Best Use of ElevenLabs — ElevenLabs Speech-to-Text converts WhatsApp voice notes into text so they can go through the same Guardian safety pipeline.
Best Use of Sentry Agent Tracing — Sentry traces Guardian's AI workflow, including message processing, URL verification, Gemma reasoning, and response formatting.
Best Use of SerpApi — SerpApi provides live web evidence for suspicious URLs before Guardian gives its assessment.

DEV Community: Shivam Singh