BeeSafe AI

Roadmap & Position in Cyber Security

Stops scams at the source by deploying AI agents that engage fraudsters and extract intelligence.

Company Overview

Builds AI agents that engage with scammers at scale to map the fraud lifecycle, extract actionable intelligence on mule accounts and attacker infrastructure, and deliver real-time fraud signals to financial institutions, telecoms, crypto platforms, and law enforcement. Pioneered the first AI system for engaging sophisticated trust-based scammers.

What They're Building

The company's public product roadmap & what they're committed to building.

AI agents that are multiplatform, multimodal, and multilingual, capable of sustaining engagement for months. Intel enabling financial services and government agencies to intercept scammers mid-transaction. Targeting financial services, crypto, telecoms, social media companies, and law enforcement.

Latest Intelligence

Zeitgeist tracks private signals to determine where the company is heading strategically.

Scammer Engagement Becomes Fraud Data

May 13, 2026

Confidence:

High

New Intel: BeeSafe AI is deploying agents that engage scammers for months to extract mule-account and attacker-infrastructure data. That turns scam baiting into a fraud intelligence product for banks, crypto, telecoms, and law enforcement.

Founder and Key Execs

Daniel Spokoyny, Co-Founder

Co-Founder

Ariana Mirian, Co-Founder

Co-Founder

Nikolai Vogler, Co-Founder

Co-Founder

Founder Force Multiplier

Three PhD founders who wrote the paper introducing the first AI system for sophisticated trust-based scam engagement. Combined 20+ years in ML, NLP, and security research across CMU, UCSD, Google, Microsoft, and Censys.

Funding History

2025 | Spokoyny, Mirian, and Vogler begin research collaboration.
2026 | BeeSafe AI founded, accepted into Y Combinator W26 batch.
2026 | Intel enabling financial services and government agencies to intercept scammers.

Competitors

Fraud Detection

Sardine, Unit21, Featurespace.

Identity Verification

Socure, Persona.

Scam-Specific

Kitboga (entertainment/awareness), various telecom scam filters.

Enterprise Security

CrowdStrike, Palo Alto Networks.

BeeSafe AI

's Moat:

Months-long scammer engagement produces intelligence on mule account networks, attacker infrastructure, and fraud lifecycle patterns that no passive detection system captures. The dataset of adversarial interactions is proprietary and grows with every engagement, making the fraud signals more accurate over time.

How They're Leveraging AI

Multi-turn adversarial dialogue

Deploys autonomous conversational AI agents that engage scammers in real-time dialogue to waste their resources, extract intelligence, and map fraudulent operations before any victim is contacted.

Graph-based fraud detection

Uses graph neural networks and anomaly detection to identify mule accounts and map interconnected scam infrastructure across financial networks in real time.

Scam typology NLP classification

Applies NLP-based classification models to incoming scam communications to automatically categorize scam type, detect novel scam scripts, and trigger early warnings for emerging fraud campaigns.

AI Use Overview:

Using adversarial dialogue agents that waste scammer resources, graph neural networks that map mule accounts, and NLP that detects new scam types within 48 hours.

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