BeeSafe AI

BeeSafe AI is a fraud prevention company whose platform aims to stop scams at their source rather than at the point of transaction. Its core approach uses "Anti-Scam Agents" that engage directly in conversation with fraudsters over the channels scammers use, eliciting the payment details and infrastructure they rely on. From those interactions the platform surfaces financial mule accounts, associated assets and infrastructure, and links them across channels and campaigns.

The company frames its target problem as trust-based, authorized-transaction fraud, which it describes as a $12B+ problem. It argues that such scams evade conventional fraud detection because victims willingly authorize payments, trust is built off-platform across multiple services so no single platform sees the full picture, and reports typically arrive only after funds are laundered, driving costly investigations and reimbursement exposure. Scam categories it cites include pig butchering, impersonation, investment, romance, employment, marketplace, lottery and crypto scams, as well as robocalls, phishing, smishing, AI voice cloning and deepfakes.

BeeSafe AI positions its output as cross-sector fraud intelligence intended to connect a fragmented fraud ecosystem, emphasizing full contextual linkage of fraudster infrastructure, evidence captured directly from adversaries, and real-time identification of active campaigns before victims are harmed.

Founding story

The company was cofounded by three researchers with doctorates in artificial intelligence and security: Daniel Spokoyny (AI PhD, Carnegie Mellon University), Ariana Mirian (Security PhD, UC San Diego) and Nikolai Vogler (AI PhD, UC San Diego). The founders cite prior experience helping financial services and government agencies intercept scammers to prevent victim losses.

Business model

BeeSafe AI supplies a fraud prevention platform and associated fraud intelligence to institutional customers, with access currently offered through an early-access request process.