Shay Levi is the Co-Founder and CEO of Unframe, an organization redefining enterprise AI with scalable, secure solutions. Previously, he co-founded Noname Security and led the corporate to its $500M acquisition by Akamai in only 4 years. A proven innovator in cybersecurity and technology, he focuses on constructing transformative solutions.
Unframe is an all-in-one enterprise AI platform headquartered in Cupertino, California, that allows businesses to bring any unique AI use case to life in hours, fairly 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 need, risk-free. Unframe is LLM agnostic and doesn’t require fine-tuning or training – foundationally changing what is feasible for big enterprises searching for scalable, turnkey AI solutions.
On April third, 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 discover within the enterprise AI space that made it the best time and opportunity?
I actually left Noname before the acquisition discussions began. What I saw was an enormous wave coming, CIOs were under pressure to adopt AI fast, however 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 construct from scratch. Neither path scaled, and each introduced risk.
That was the signal. I noticed enterprises didn’t just need access to AI – they needed a platform that gave them control, speed, and adaptability at the identical time. That’s what led to Unframe.
Noname Security was a pioneer in API cybersecurity. How has your experience constructing 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 results in risk. That lesson carries over on to AI. From day one at Unframe, we’ve baked in the best guardrails – secure data handling, model transparency, role-based access – so enterprises can innovate without introducing latest vulnerabilities.
We’re also very aware of how things break at scale. So while Unframe empowers teams to maneuver 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 within the API security space that helped inform your vision for AI adoption?
Definitely. At Noname, we saw how difficult it was for enterprises to achieve visibility and control across their environments. Shadow APIs, inconsistent tooling, and siloed teams created real operational risk – and it slowed all the pieces down.
With AI, we’re seeing the identical pattern repeat. Every team wants to maneuver quickly, but without the best structure, you get fragmentation, duplication, and blind spots. That have shaped our pondering with Unframe. We wanted to offer enterprises a solution to adopt AI in a way that’s unified, secure, and really works across teams and systems – not only in isolated pockets.
Unframe is gaining traction with major enterprises and achieved ARR within the hundreds of thousands inside a 12 months – how did you achieve this level of adoption so quickly?
We didn’t take the standard route of slow experimentation or limited pilots. From day one, we were out out there, 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 change into a strategic partner across multiple domains, not only a vendor. Whenever you deliver real outcomes fast, adoption follows.
You’ve spoken about reducing AI deployment from months to hours. Are you able to walk us through how Unframe makes this possible?
We’ve built a whole lot of deep technical constructing blocks into the Unframe platform. When a brand new solution is deployed, it’s not ranging 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 concerning 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 ranging from scratch. Each blueprint maps the logic, components, workflows, and guardrails for a selected use case, configuring our platform’s library of technical constructing blocks. It’s how we mix speed and precision at scale.
Unframe positions itself as LLM-agnostic and doesn’t require model fine-tuning. Why was it vital so that you can avoid the necessity for training custom models?
Because most enterprises don’t need custom models – they need custom outcomes. The second you begin fine-tuning, you’re locking yourself into a selected vendor, increasing costs, and creating maintenance overhead that the majority organizations aren’t set as much as handle.
We designed Unframe to work with existing modern models in a way that also delivers tailored, high-quality results – without the complexity. By staying LLM-agnostic, we give enterprises flexibility, faster time to value, and the flexibility to evolve because the model landscape changes. The goal isn’t to coach models – it’s to unravel problems. And you may try this incredibly well without touching fine-tuning.
What role does natural language interaction play in Unframe’s platform – and the way far can it go in replacing traditional software UIs?
Natural language is a strong entry point – it makes AI immediately accessible to business users, without training or technical ramp-up. That’s especially vital once you’re working with global corporations 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 must match the duty. Sometimes meaning a natural language chat. Other times, it’s a dynamic table, an interactive dashboard, or a content generation interface – whatever most closely fits the workflow and the consequence we’re solving for.
We don’t see natural language as a substitute 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 only to the user, but to the issue they’re trying to unravel.
What lessons from scaling Noname Security to a $1B+ valuation and $450M acquisition are you applying at Unframe?
Give attention to 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 very same mindset: move quickly, construct securely, and stay relentlessly customer-focused.
As a repeat founder, what’s your approach to constructing leadership teams and company culture in hyper-growth environments?
In hyper-growth, you don’t have the posh of figuring things out slowly – so you wish people around you who aren’t only great at what they do, but who thrive in ambiguity and move with urgency. For me, constructing a leadership team starts with trust, clarity, and shared values. Everyone must be aligned on where we’re going, and equally committed to owning their a part of the journey.
Culture is similar. It’s not ping-pong tables – it’s the way you make decisions when things get hard. At Unframe, we’ve been intentional about making a culture of ownership, pace, and honesty. We move fast, we listen hard, and we push one another to be higher day-after-day. That form of culture doesn’t just survive hyper-growth – it drives it.
