Mesh is a San Francisco-based fintech company building the infrastructure to power a global crypto payments network. After a Series B that brought total funding past $120 million, the company scaled quickly, growing beyond 100 employees and building integrations across major financial and identity platforms. That same growth made Mesh a high-value target: a larger team, a broader digital footprint, and deep ties to the financial system gave attackers more surface area to probe.

As Mesh grew, the attacks changed shape. What reached inboxes no longer looked like traditional phishing. Adversaries sent highly specific impersonations, from CEO impersonation to invoice fraud to onboarding scams, combining generative AI with real-world context to produce increasingly realistic lures. Three problems stood out: generative impersonation that mimicked internal teammates and trusted vendors; attacks from legitimate but compromised servers that sailed past reputation and signature defenses; and the need for data-loss prevention to keep sensitive payment data and credentials from leaking. The sophistication had outrun what rule-based tools could handle.

Mesh turned to AegisAI, an email security platform built on agent-native AI designed to detect, reason, and act against advanced threats. Rather than relying on static rules, AegisAI deploys autonomous agents that understand context, model behavior, and adapt to novel attacks, reasoning through intent instead of scanning only for known-bad patterns. That lets it catch zero-day phishing, new BEC variants, and threats never seen before, exactly what was reaching Mesh. It also met Mesh's privacy requirements: real-time scanning inline with the Google Workspace APIs, retaining only metadata or flagged content. For a crypto-native company, that balance of strong protection and minimal data retention was a deciding factor.

Because AegisAI's agents reason through intent and context, they catch threats tailored to a specific company. One arrived on the Fourth of July. Mesh had recently posted about a crypto event it was attending in France. An attacker combined that public detail with a compromised French events company's website and identity to send a targeted payment request to Mesh's marketing team, carrying a fake CAPTCHA lure and a Google phishing page built to harvest credentials. AegisAI recognized the request for what it was and quarantined it automatically. There was no payment to reverse, no pager alert, and no user compromise. The security team came back to a clear alert and report: a clean account of an evolving threat, handled while they were out.

Since deploying AegisAI, Mesh has seen a 99.7% reduction in employee exposure to email-based threats, with zero user-reported phishing incidents, no missed legitimate business emails, zero manual tuning, and a 90% reduction in time to detect and respond. Just as important, AegisAI freed the team to focus: with email risk largely out of the equation, Mesh's security team could concentrate on scaling infrastructure and product securely instead of chasing inbox threats.

In a space as fast-moving and target-rich as crypto fintech, static defenses are no longer enough. Mesh chose AegisAI for its detection results and for an architecture designed to adapt as fast as attackers do: a shift from rule-based blocking to context-aware, autonomous defense. For Mesh, that meant fewer alerts, fewer incidents, and a safer path to global scale. As Mesh CEO Bam Azizi put it: "As a former security founder, I've seen the cat-and-mouse game play out for decades, especially in email security, where attackers constantly evolve to trick employees. Aegis is the first solution that truly changes the game. They came into Mesh and stopped attackers in their tracks. Our dashboard shows everything from fuzzing attempts to AI-generated spear phishing and BEC, and Aegis catches them all, without my team wasting time managing rules."