Fighting online fraud with FraudTalon

After getting so many messages from my parents, wife, sister, and friends asking if emails or ads they saw on social media were legit, I decided to build a tool to help identify fraud, scams, and phishing attempts.

That’s how FraudTalon was born.

It’s currently in MVP version 0.0.1 — basic functionality, simple heuristics (I started with NLP but dropped it — not needed for now), and a single cloud-based LLM. The goal at this stage is to validate the idea.

I built it using tools I know well: Python, Django, PostgreSQL, Celery, and OpenAI. But I want this to grow into a multi-LLM platform. I’m not interested in depending on black-box, closed, expensive services hosted in someone else’s cloud. I wrote more about this mindset in my SovereignRAG project.

No no-code/low-code, no n8n-style flows. Didn’t need them. And yeah, I’ve got some bias against no-code — I’m an old-school dev — just like I had bias against AI a year ago. Things change.

Roadmap

• Upload and analysis of .eml files
• URL validation through external sources like SpamHaus
• Phone number checks
• QR code inspection

Right now, you can paste the raw body of an email and get an instant risk analysis. But that’s just the beginning.

This is a tool for devs, security people, curious minds, and anyone who’s ever seen someone fall for a scam.

Let’s make the internet a little less dangerous.
One analysis at a time.

Watch the youtube video (pt_BR only, sorry).