TL;DR: BeeSafe stops scams at the source by engaging with scammers to identify and shut down the fraudulent accounts they use for their operations. We are Ariana Mirian, Nikolai Vogler, and Daniel Spokoyny, three PhD AI & security experts, and we pioneered the first AI system for engaging sophisticated, trust-based scammers—now we’re scaling it into a product that helps:

• Financial Services & Crypto detect mule accounts for AML compliance
• Telecommunications block suspicious inbound messages
• Government Agencies take down cybercriminal operations

The Problem: The $12B Visibility Gap for Financial Institutions

Have you ever received a text like this?

This seemingly harmless message is the start of a sophisticated social engineering scam, sometimes referred to as “pig butchering”. Scammers strike up a conversation to build trust with their victims over time, eventually convincing them to authorize real-time, irreversible payments via P2P apps, crypto, or wire transfers:

$12B was reported stolen in trust-based scams like this in the US last year, according to the FTC. With multilingual LLMs, deepfakes, and voice cloning, trust-based scam losses are skyrocketing.

Financial Services, Telcos, and Government Agencies have little visibility into trust-based scams because attackers bypass traditional fraud prevention systems (e.g. behavior-based anomaly detection):

• Off-Platform Trust Building: Conversations are held across multiple, end-to-end encrypted platforms, leaving any one platform blind
• Authorized Transactions: Victims are manipulated over time into trusting the attackers so they authorize transactions
• Reactive Investigations: Enterprises only know about the scams after a victim reports lost money (if they report it at all)

The Solution: AI-Powered Scam Disruption

Instead of blocking scammers, our system intercepts and engages with them to extract intelligence, including financial mule accounts, fraudulent assets, malicious domains, crypto drainers, and other related infrastructure.

Our intel’s edge:

• Full Campaign Context: we map the entire fraud operations, from initial message to financial exfiltration, allowing us to map patterns of attacks.
• Verified Data: Our intelligence represents ground truth, and is not based on a probabilistic risk score. We minimize false positives by capturing details directly from the attackers.
• Real Time-Discovery: all attacker infrastructure is discovered in real-time, preventing real-victim losses.

We provide instant access to high-fidelity fraud signals for B2B and deliver this to our customers in a way that does not add any friction to everyday users.

We have already enabled financial services and government agencies to identify and intercept scammers pre-transaction to prevent victim losses, with 10,000s of real scammer conversations across communication channels, and 1,000s of mule accounts + linked infrastructure already.

Our asks:

If your organization and customers are facing trust-based fraud, we want to meet and see how BeeSafe can help.

Please reach out if you work in

• anti-fraud, anti-money laundering, or compliance organization
• consumer-facing fintechs, crypto exchanges, or telcos

by sending us a message at founders@beesafe.ai or visiting beesafe.ai to learn more.

About Us: We are Ariana Mirian, Nikolai Vogler, and Daniel Spokoyny a team of three PhD founders from Carnegie Mellon and UC San Diego. We wrote the paper that introduced the first AI system for sophisticated, trust-based scams. We bring expertise in building AI systems for security—grounded in core machine learning research and real-world cybercrime defense—with industry research experience from Google, Microsoft, Censys, and major DoD-funded programs.