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 ~1,400 patent applications and 10+ outside law firms 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.

Different Security Models, Same Fundamental Problem

Over 13+ years at companies ranging from Big Law to Big Tech—including Apple, Google, Amazon, and enterprise SaaS platforms—I built legal operations infrastructure in organizations with fundamentally different approaches to risk and security.

Some companies operated on trust-and-verify models where access was presumed until proven problematic. These environments enabled incredible velocity—teams could move fast, experiment freely, and innovate without friction. But they required strong oversight mechanisms and monitoring to catch problems before they compounded.

Other companies used restrict-first approaches where permissions were explicitly granted layer-by-layer. These environments reduced risk dramatically—every access request was scrutinized, every integration required approval, every new tool went through extensive vetting. But they created operational friction that could slow transformation to a crawl if not managed carefully.

Here's what I realized: Neither approach is inherently better. They're different risk/velocity tradeoffs based on organizational culture, regulatory requirements, and past experiences. A financial services company with stringent compliance obligations should operate differently than a fast-moving consumer tech startup.

What I saw repeatedly: organizations in both types of environments would deploy AI tools without first diagnosing whether their specific structure, governance model, and employee capabilities could support those tools. A trust-and-verify company would move fast but lack governance to catch AI risks. A restrict-first company would have strong oversight but couldn't move quickly enough to capitalize on AI opportunities.

The solution isn't picking a "better" security model. It's assessing readiness within your existing model, then redesigning your organization for the AI era.

Fortune 500s Were Asking the Wrong Question

Across multiple Fortune 500 tech companies—from product development to enterprise SaaS—I watched billion-dollar organizations 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 diagnoses organizational readiness before AI tools go live, then teaches organizations how to redesign their operations for the AI era. This isn't about accepting your constraints—it's about understanding your starting point (trust-and-verify vs. restrict-first, startup vs. enterprise, regulated vs. unregulated) so we can design the right transformation for your context.

That transformation includes fundamentals most companies overlook: what new roles you need to create (including the Intelligence Resources function itself), what skills to hire for, how decision rights should change, how workflows must evolve, and what governance structures actually work in practice. Intelligence Resources™ isn't a framework you bolt onto existing operations—it's a new corporate function that teaches organizations how to operate differently in the AI era.

Whether you operate trust-and-verify or restrict-first, whether you're a fast-moving startup or a heavily regulated enterprise, Intelligence Resources™ helps you understand exactly what needs to be in place before deployment—then shows you how to redesign your organization to get there.

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 across different security philosophies 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.