Shay Levi is the Co-Founder and CEO of Unframe, a company redefining enterprise AI with scalable, secure solutions. Previously, he co-founded Noname Security and led the company to its $500M acquisition by Akamai in just four years. A proven innovator in cybersecurity and technology, he specializes in building transformative solutions.
Unframe is an all-in-one enterprise AI platform headquartered in Cupertino, California, that enables businesses to bring any unique AI use case to life in hours, rather than months. Through its Blueprint Approach, Unframe collaborates with large enterprises globally to implement solutions across observability, data abstraction, intelligent agents, and modernization. Unframe uses outcome-based pricing, allowing customers to experience and evolve any solution they want, risk-free. Unframe is LLM agnostic and doesn’t require fine-tuning or training – foundationally changing what is possible for large enterprises seeking scalable, turnkey AI solutions.
On April 3rd, 2025, Unframe Emerged from Stealth with $50M to Transform Enterprise AI Deployment.
Following the successful exit of Noname Security to Akamai, what motivated you to launch Unframe, and what gap did you identify in the enterprise AI space that made it the right time and opportunity?
I actually left Noname before the acquisition discussions started. What I saw was a massive wave coming, CIOs were under pressure to adopt AI fast, but the tooling available to them just wasn’t enterprise-ready. They were either piecing together point solutions with no governance, or waiting on internal teams to build from scratch. Neither path scaled, and both introduced risk.
That was the signal. I realized enterprises didn’t just need access to AI – they needed a platform that gave them control, speed, and flexibility at the same time. That’s what led to Unframe.
Noname Security was a pioneer in API cybersecurity. How has your experience building a security-focused company shaped the approach you’re taking with Unframe?
Security is in our DNA. At Noname, we learned that innovation without governance quickly leads to risk. That lesson carries over directly to AI. From day one at Unframe, we’ve baked in the right guardrails – secure data handling, model transparency, role-based access – so enterprises can innovate without introducing new vulnerabilities.
We’re also very aware of how things break at scale. So while Unframe empowers teams to move fast, we’ve designed the platform with enterprise complexity in mind – whether it’s managing data flows, enforcing compliance, or integrating with legacy systems.
Were there any common pain points across enterprises in the API security space that helped inform your vision for AI adoption?
Definitely. At Noname, we saw how challenging it was for enterprises to gain visibility and control across their environments. Shadow APIs, inconsistent tooling, and siloed teams created real operational risk – and it slowed everything down.
With AI, we’re seeing the same pattern repeat. Every team wants to move quickly, but without the right structure, you get fragmentation, duplication, and blind spots. That experience shaped our thinking with Unframe. We wanted to give enterprises a way to adopt AI in a way that’s unified, secure, and actually works across teams and systems – not just in isolated pockets.
Unframe is gaining traction with major enterprises and achieved ARR in the millions within a year – how did you achieve this level of adoption so quickly?
We didn’t take the traditional route of slow experimentation or limited pilots. From day one, we were out in the market, partnering with global enterprises on high-impact, real-world projects. These weren’t isolated use cases – they were strategic initiatives tied to core parts of the business. That’s what earned us trust and helped Unframe become a strategic partner across multiple domains, not just a vendor. When you deliver real outcomes fast, adoption follows.
You’ve spoken about reducing AI deployment from months to hours. Can you walk us through how Unframe makes this possible?
We’ve built hundreds of deep technical building blocks into the Unframe platform. When a new solution is deployed, it’s not starting from zero – it’s tailored through a blueprint that maps those components to the user’s specific needs. That’s how we reduce deployment from months to hours.
Tell us more about the Blueprint Approach – what makes it unique, and why is it so powerful for enterprise AI use cases?
The Blueprint Approach is how we deliver tailored AI solutions at scale – without starting from scratch. Each blueprint maps the logic, components, workflows, and guardrails for a specific use case, configuring our platform’s library of technical building blocks. It’s how we combine speed and precision at scale.
Unframe positions itself as LLM-agnostic and doesn’t require model fine-tuning. Why was it important for you to avoid the need for training custom models?
Because most enterprises don’t need custom models – they need custom outcomes. The second you start fine-tuning, you’re locking yourself into a specific vendor, increasing costs, and creating maintenance overhead that most organizations aren’t set up to handle.
We designed Unframe to work with existing modern models in a way that still delivers tailored, high-quality results – without the complexity. By staying LLM-agnostic, we give enterprises flexibility, faster time to value, and the ability to evolve as the model landscape changes. The goal isn’t to train models – it’s to solve problems. And you can do that incredibly well without touching fine-tuning.
What role does natural language interaction play in Unframe’s platform – and how far can it go in replacing traditional software UIs?
Natural language is a powerful entry point – it makes AI instantly accessible to business users, without training or technical ramp-up. That’s especially important when you’re working with global companies and distributed workforces across different countries, roles, and languages. A chat-style interface helps level the playing field.
But every Unframe use case is different, and the interface needs to match the task. Sometimes that means a natural language chat. Other times, it’s a dynamic table, an interactive dashboard, or a content generation interface – whatever best fits the workflow and the outcome we’re solving for.
We don’t see natural language as a replacement for traditional UIs, but as a layer that removes friction where it matters. The goal is to make software feel intuitive, flexible, and tailored – not just to the user, but to the problem they’re trying to solve.
What lessons from scaling Noname Security to a $1B+ valuation and $450M acquisition are you applying at Unframe?
Focus on solving real, urgent problems – and do it with enterprise-grade execution from day one. At Noname, we learned that scale comes from trust, and trust comes from delivering fast without cutting corners. At Unframe, we’re applying that same mindset: move quickly, build securely, and stay relentlessly customer-focused.
As a repeat founder, what’s your approach to building leadership teams and company culture in hyper-growth environments?
In hyper-growth, you don’t have the luxury of figuring things out slowly – so you need people around you who are not only great at what they do, but who thrive in ambiguity and move with urgency. For me, building a leadership team starts with trust, clarity, and shared values. Everyone has to be aligned on where we’re going, and equally committed to owning their part of the journey.
Culture is the same. It’s not ping-pong tables – it’s how you make decisions when things get hard. At Unframe, we’ve been intentional about creating a culture of ownership, pace, and honesty. We move fast, we listen hard, and we push each other to be better every day. That kind of culture doesn’t just survive hyper-growth – it drives it.
Thank you for the great interview, readers who wish to learn more should visit Unframe.