Built in the San Francisco Bay Area, Forged in High-Stakes Environments

Companies Deploy AI—Then Realize They Weren't Ready

Across multiple high-scale tech companies—from product development to enterprise SaaS—I watched the same pattern unfold: organizations would roll out AI tools, and then everything would become messy. Teams would realize too late that the underlying organizational structures weren't designed for AI. Employees lacked the skills to use it effectively. Governance frameworks didn't exist.

The chaos wasn't the AI itself—it was deploying intelligent systems into organizations that weren't ready for them. And retrofitting readiness after deployment is exponentially harder than building it in from the start.

Everyone was racing to adopt AI without asking the foundational question: Is our organization actually prepared to support this?

Learning Precision in High-Stakes Environments

My career began at Haley Guiliano, a Ropes & Gray spinoff, where I spent six years as a Senior Patent Paralegal managing global operations for tech giants including Google. Managing 500+ complex jurisdictional filings taught me something fundamental: In high-velocity environments, a 0.1% error rate in the foundation leads to 100% failure at scale.

One missed deadline in patent law doesn't just delay a project—it can invalidate years of R&D investment. One misrouted document doesn't slow things down—it creates legal exposure that compounds. This "zero-defect" culture became the lens through which I viewed every system I touched afterward.

Later, at Amazon Lab126, I managed a patent portfolio with 1,200+ yearly filings while building operational frameworks that bridged product innovation with legal and compliance requirements. That's when I realized: The companies that scale successfully don't retrofit compliance—they architect it into the foundation.

Fortune 500s Were Asking the Wrong Question

As Principal Legal Consultant at Legal.io, I deliver end-to-end oversight for complex, multi-year infrastructure initiatives at Fortune 500 organizations. I've seen billion-dollar companies stumble over the same question: "How do we deploy AI faster?"

The better question is: "Are we ready for what happens after we deploy it?"

That gap—between deployment speed and organizational readiness—is where Innovation Teams burn resources cleaning up post-deployment chaos, where Compliance becomes an emergency response team instead of a strategic partner, and where Legal Operations scrambles to create guardrails that should have existed from day one.

Organizations don't need faster AI deployment. They need to assess whether they have the Intelligence Resources™ to support AI sustainably—before things get messy.

Intelligence Resources™: Readiness Before Deployment

I founded Ciph Lab to solve the root cause: enterprises lack a systematic way to assess AI readiness before deployment—so they roll out tools into unprepared organizations and spend years managing the aftermath.

Intelligence Resources™ is a structured methodology that helps organizations understand exactly what needs to be in place before AI tools go live. It's the diagnostic layer that reveals whether your structure, governance, and employee capabilities can support AI—or whether you're about to create expensive chaos.

My approach combines a B.S. in Legal Studies, UC Berkeley Business Administration graduate certificate, and MBA training (University of the People, graduating 2027) with hands-on experience building operational frameworks in high-stakes, highly-regulated environments where precision isn't optional.

Ciph Lab exists because the gap between AI deployment and organizational readiness is too expensive to ignore—and too predictable to keep repeating.