$100 million and 400% growth in 12 months: How the startup Unframe is solving the biggest AI problem for corporations

This “AI operating system” is what makes companies truly profitable

The hype surrounding artificial intelligence in the corporate world is deafening, yet the reality on the balance sheets is often sobering. While billions are poured into testing generative AI, the vast majority of large companies fail to translate their flagship projects into productive, value-creating operations. It is precisely into this gaping chasm between technological promise and operational stagnation that the startup Unframeis stepping. With a radical approach that sells results instead of mere licenses, and an architectural system that reduces deployment times from months to days, the founding team is redefining the enterprise software market. The economic response has been unprecedented: $100 million in contract volume in just twelve months, a near-mythical net revenue retention of 400 percent, and a fresh $50 million funding round led by Highland Europe. But what is really behind these exceptional metrics, and why could Unframe's "managed delivery" model mark the beginning of the end of the classic SaaS era?

In just twelve months, Unframe has surpassed $100 million in Total Contract Value (TCV), achieved a 400% Net Revenue Retention, and expanded its presence with companies in global markets. This milestone positions us as one of the fastest-scaling enterprise AI companies ever. More importantly, it reflects a broader shift taking place among Fortune 500 companies: businesses are finally moving AI from mere ambition to actual implementation.

To further accelerate this momentum, Unframe has also announced an additional funding round of $50 million. This round is led by Highland Europe, along with existing investors Bessemer Venture Partners, Craft Ventures, TLV Partners, Third Point Ventures, Cerca Partners, and Vintage Investment Partners. This brings Unframe 's total funding to $100 million.

When a $100 million contract volume tells more than any glossy brochure

Rarely is the gap between aspiration and reality as dramatic in a technology market as in the field of artificial intelligence for large corporations. According to McKinsey's latest global survey, 88 percent of all organizations are already regularly using AI in at least one business function – a significant increase from 78 percent the previous year. But this seemingly triumphant adoption rate is deceptive: Only one percent of these companies describe their AI rollout as truly "mature," and a mere six percent are among the so-called high performers that actually derive measurable financial returns from their AI investments. The discrepancy between widespread use and productive, value-creating operation is therefore not just a technical problem – it is a fundamental strategic and entrepreneurial failure that materializes in billions of dollars in wasted investment.

The paradox becomes even more pronounced when looking at the figures for production readiness: While research by MIT Sloan Management Review shows that 39 percent of companies are now using AI in production – a significant improvement compared to 24 percent last year and less than five percent two years ago – this also means that 61 percent of companies are still stuck between the experimentation and deployment phases. Deloitte's State of AI 2026 confirms this picture: Only 25 percent of organizations have moved more than 40 percent of their AI pilots into production, and only 34 percent are using AI to fundamentally transform their business. McKinsey's analysis of a similar study goes even further: Of all enterprise AI initiatives, only 27 percent even reach the production readiness stage, and of these 27 percent, 15 percent are shut down again within twelve months – reducing the true success rate to a mere twelve percent.

The financial dimension of this failure is considerable. The global enterprise AI market was projected to reach an estimated $107 billion in 2025. Private investment in generative AI is estimated at around $62 billion for 2025 – a 94 percent increase over the previous year. In this environment, funds are not only being spent on working solutions, but also, to an alarming extent, on projects that never progress beyond the proof-of-concept phase. Unframe positions itself precisely in this structural gap between investment readiness and operational capability – and this is precisely where the economic weight of its recent announcement lies.

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• UNFRAME.AI: A Defining Moment for Unframe and Enterprise AI

Who is behind Unframe : Founders with proven decoding skills in complex systems

To assess the credibility of the Unframe story from an economic perspective, it's worth looking at the people behind it. CEO and co-founder Shay Levi is no newcomer to pushing into the market with AI promises. Levi previously co-founded Noname Security – a company he grew to $40 million in ARR within four years before selling it to Akamai for $500 million, thereby earning the title of the first API cybersecurity unicorn. Before that, Levi was a software engineer at Facebook and a graduate of the Israeli intelligence unit 8200, which is considered a breeding ground for security and technology founders worldwide.

Alongside him are COO Larissa Schneider, the German co-founder who lends Unframe a European profile and brings experience from corporate management and IPO processes, and VP of R&D Adi Azarya, also a veteran from the Noname Security team. The trio thus brings a rare combination of technical depth, sales power, and entrepreneurial maturity to a market traditionally dominated by large platform providers. Unframe has deliberately established a global operational footprint: headquarters in Cupertino, California, technical development in Tel Aviv, and a presence in Berlin that secures access to the European corporate market.

The founding team has amassed relevant experience that is particularly pertinent to the specific problem: enterprise software is rigid, slow, and not impact-oriented. Levi describes the motivation for founding the company as a shared frustration with the traditional model: too niche, too slow, and offering too little value. This frustration is not internal, but rather distilled from thousands of conversations with enterprise customers—a crucial difference compared to technology-driven startups that seek solutions before they have truly understood the problem.

$100 million in TCV in twelve months: What this number really means

On May 19, 2026, Unframe announced that it had accumulated $100 million in total contract value (TCV) over the preceding twelve months – and simultaneously closed a new $50 million funding round led by Highland Europe, bringing the company's total capital to $100 million as well. This parallel is no coincidence: it illustrates the speed at which capital market-based valuations and actual customer revenues are converging in this segment.

But what does the TCV figure mean in context? Total Contract Value is not the same as Annual Recurring Revenue (ARR). TCV encompasses the total volume contracted over the contract terms—a sum that includes multi-year contracts in their entirety. The distinction is material, as TCV figures appear larger than ARR figures. The Next Web also points out that the 400 percent figure for Net Revenue Retention is based on internal measurements and has not been independently audited. Despite these necessary methodological limitations, the pace of traction gain is exceptional: Unframe generated millions in ARR in its first quarter after its stealth exit in April 2025, and prominent Fortune 500 companies such as Cushman & Wakefield and Nomura were secured early as reference customers.

The quality of the investor syndicate underscores the company's credibility: Bessemer Venture Partners, Craft Ventures, TLV Partners, Third Point Ventures, Cerca Partners, Vintage Investment Partners, and, most recently, Highland Europe are backing the company. Bessemer Venture Partners, in particular, is considered one of the most astute analysts of SaaS metrics worldwide – their continued involvement with Unframe is a signal of quality that goes beyond typical venture capital marketing.

The Deployment Problem: Why Enterprise AI Keeps Failing at the Production Threshold

To fully understand Unframe's market position, one must understand the structural causes of enterprise AI failure. The common explanation—a lack of model maturity or management skepticism—falls short. The analytics platform Cephable identifies three deeper systemic causes: First, the workflow integration problem: AI is tacked onto existing processes as an add-on, rather than embedded within them. Users have to interrupt their actual workflow to consult AI tools separately—a friction loss that, over hundreds of daily interactions, adds up to a significant productivity loss. Second, the deployment flexibility problem: The market has invested too heavily in cloud-based orchestration of complex multi-agent systems, while 84 percent of actual production deployments are architecturally simple. And thirdly, a deep data problem: As one executive quoted at a panel event with representatives from Rippling, Workday and ServiceNow put it, 70 percent of the work on enterprise AI projects is spent on data preparation alone – a vastly underestimated task for most project managers.

Added to this is the institutional inertia of the procurement process. Typical enterprise AI projects go through a procurement cycle of up to 24 months: from the initial pilot through budget approvals, vendor selection, legal review, security review, to final production rollout. Integration costs alone can range from $20,000 to $50,000 for a single system—for a typical large enterprise with seven or more core systems, integration efforts add up to $140,000 to $350,000 before a single line of production AI logic has even been written. Furthermore, security concerns represent the final deal-breaker in 30 percent of cases: unclear data access rights, risks to personal data in model outputs, and regulatory requirements.

This mountain of structural complexity is the real market failure that Unframe addresses. And it explains why, despite an 88 percent adoption rate, only one percent of companies can describe their AI operations as mature.

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The Framery as an architectural core: An economic evaluation of the platform approach

What technically and economically distinguishes Unframe from generic AI platforms is the architecture of its core system, marketed as "The Framery." The platform is designed as an "OS for Production AI"—an operating system that assembles production-ready AI from pre-configured, proven building blocks. The four central architectural elements are: an agent orchestrator with built-in guardrails and full observability; a knowledge fabric as a context layer that enriches enterprise data with business logic; a data connectivity layer with pre-built integrations for any system and environment; and a modular building block system that covers search, reasoning, automation, and agentic workflows.

The economic logic behind this approach is compelling: Every new solution a company commissions from Unframe benefits from the contextual knowledge already built up in previous deployments. The first deployment takes days, the fifth is completed in hours. This compounding logic—the accumulation of economic value through successive deployments—is the real driver of the exceptional net revenue retention. When each new solution doesn't start from scratch but builds upon an already established, company-specific context layer, deployment costs are reduced, precision is increased, and a strong switching barrier is created. In the jargon of platform economics, this is referred to as a data network effect in an enterprise environment: The value of the system increases with each use case, without a proportional increase in costs.

Unframe deliberately remains LLM-agnostic – independent of any specific language model – and supports deployment in the cloud, on-premises, or in hybrid environments. This neutrality is strategically important in a market where enterprise customers, faced with regulatory requirements and data privacy concerns, do not want to become dependent on individual model provider platforms. Furthermore, Unframe avoids upfront commitments: customers only pay when they actually see results – a pricing model that shifts the risk to the provider and significantly lowers the barrier to entry for enterprise customers.

400 percent net revenue retention: A statistical outlier with economic implications

The published Net Revenue Retention Rate (NRR) of 400 percent warrants its own analytical examination, as it is one of the most well-known SaaS metrics, and Unframe's figure is exceptional compared to all known benchmarks. For reference, an NRR of 118 percent is considered a top-quartile value for enterprise SaaS companies, while a rate of 108 percent represents a solid mid-range performance. Even among the world's best SaaS companies—including Snowflake in its early growth phase and Veeva Systems—values ​​above 130 percent are considered exceptional, and those above 150 percent are seen as almost mythical.

An NRR of 400 percent essentially means that existing customers expand their contract volume with Unframe by an average of four times the original value – even when accounting for customer churn. This figure can only be explained by a specific mechanism: Companies that launch their first Unframe use case immediately roll out the system to numerous other operational areas. The platform architecture – once integrated, its cumulative effect – creates a pull toward internal scaling, leading to several times the initial volume within just a few months. As The Next Web correctly points out, this is an internal figure and not externally audited – which is methodologically transparent for a 14-month-old company with a still small customer base and few cohort cycles. Nevertheless, even with significant adjustments for statistical caution, such a starting value indicates an exceptionally high product-market response, which is reflected in the customers' expansion behavior.

The return-on-investment discourse: AI between hype cycle and measurable value

The investor landscape surrounding enterprise AI is characterized by fundamental ambiguity, reflected in the public ROI debate. McKinsey's data for the second half of 2024 shows some encouraging signs: In strategy and corporate finance, 70 percent of respondents reported revenue increases through the use of AI, in supply chain management 67 percent, and in marketing 66 percent. At the same time, most companies are achieving improvements in the range of less than five percent, and the proportion of those with more than ten percent revenue growth remains in the single digits in most functions.

The criticism of short-term ROI thinking is not unfounded. Compared to historical technology waves – ERP systems from the 1990s, cloud computing from the 2000s, CRM implementations with their 50 to 70 percent failure rate – the demand for fully measurable AI ROI within two years seems structurally unrealistic. However, those who, like Unframe , focus on outcome-based pricing and time-to-value measured in days fundamentally change this discourse. When a business customer doesn't have to wait months or years for initial results, but instead sees a productive solution running in their own infrastructure within a week, the ROI discussion shifts from the theoretical business case to empirical measurement.

Planet Crust Research estimates the typical ROI for mid-market companies implementing successful enterprise AI solutions at 200 to 400 percent over three years, with a payback period of eight to 15 months. For large enterprises with over 1,000 employees, payback periods typically range from 15 to 24 months due to greater complexity. Unframe's model—no upfront commitment, deployment in days, and incremental expansion—is designed to structurally shorten this payback period, thereby reducing investment resistance among enterprise decision-makers.

The investor pull: What Highland Europe's leadership is signaling to the group

The composition and structure of the current funding round is a subject of analysis in its own right for market observers. The fact that Highland Europe – a growth-focused fund with proven expertise in the B2B software market – is leading the Series B round is no coincidence. Growth capital of this caliber is typically only mobilized once go-to-market mechanisms have been demonstrated and scaling pathways with an acceptable risk profile are clearly identifiable. Highland Europe's investment suggests that Unframe has passed precisely this test for them.

The re-participation of all previous investors – Bessemer Venture Partners, Craft Ventures, TLV Partners, Third Point Ventures, Cerca Partners, and Vintage Investment Partners – is another significant signal. Insider re-investments, meaning the renewed participation of existing investors in a subsequent funding round, are one of the strongest positive signals in the venture capital market because these investors possess informational advantages that are inaccessible to external observers. The fact that not a single early investor has withdrawn or declined to participate in the follow-up round speaks to a consistent internal and external conviction in the company's development direction.

According to the company, the new capital will be invested in three areas: expanding go-to-market capabilities, deepening platform investments, and expanding the senior leadership team. This prioritization is economically sound: In a market where demand exceeds supply – as Unframe itself diagnoses for the enterprise AI sector – the limiting factor is not the technology, but the ability to scale sufficiently quickly and deliver high-quality customer projects.

Managed Delivery as a business model: Between SaaS and professional services

Unframe's positioning as a "Managed AI Delivery Platform" is economically ambivalent—and this is by design. The company is neither a classic SaaS provider that scales software on a self-service basis, nor a traditional consulting firm that sells hours of service. It operates in a hybrid space: a technology-driven platform with human input of solution intelligence. As Philip Lockhard of Credera put it: Unframe doesn't simply provide a tool, but rather brings the thoughtfulness and partnership that drive real results. This partnership approach is a deliberate cultural departure from a pure license-sales model.

Economically, this hybrid approach has advantages and disadvantages. On the advantages side are a higher average contract value, stronger customer loyalty, and—as the NRR figures suggest—significant expansion potential. On the disadvantages side is a scaling model that places greater demands on human resources than pure software platforms. The more Unframe grows, the more crucial the question becomes of how the delivery aspect of the business model can be automated and scaled without undermining its quality commitments. The blueprint architecture with pre-configured building blocks is the technical answer to this scaling challenge: It attempts to systematize the transfer of knowledge from one use-case implementation to the next, thereby combining human expertise with platform efficiency.

Competitive dynamics: Unframe in the field of enterprise AI platforms

The enterprise AI market is not a homogeneous field. Unframe is not pitted against a single competitor, but against a broad spectrum of different solution approaches. On the one hand, there are horizontal AI platform providers like Microsoft Azure AI, Google Cloud Vertex AI, and Amazon Bedrock, which boast enormous infrastructure and ecosystem depth, but leave the challenge of finding the solution itself to the customer. On the other hand, there are point solutions – focused AI applications for specific functions such as sales, customer service, or HR – which are quick to implement, but remain in isolated silos and fail to develop integrated intelligence across business processes.

Unframe deliberately positions itself between these two extremes: more comprehensive than a point solution, more concrete and faster than a generic infrastructure platform. The comparison drawn by Credera CDO Lockhard – “build, buy, or borrow” – illustrates the strategic logic from the customer's perspective. Unframe is the clearly defined “buy” path for companies that lack the resources to build full enterprise AI expertise internally, nor are willing to settle for a generic tool lacking operational depth. This niche holds strategic promise as long as the major cloud providers fail to develop comparably fast and customized delivery capabilities – a structural advantage that forms a natural protected zone in the premium segment of the market.

A structural shift in the enterprise software market

What Unframe's success means in a broader context can be summarized with a simple thesis: The enterprise software market is currently undergoing a fundamental redefinition of what "product" means. In the classic SaaS era, a product was a software application that customers configured and used themselves. In the AI ​​era, the product promise is shifting toward the outcome: It's not the license that's being sold, but the solution. Not the tool, but the result. This shift is profound because it fundamentally changes the contract, pricing, and delivery model—and forces established providers to rethink their entire go-to-market model.

Grand View Research estimates the global AI market at $390 billion in 2025, with projected growth to $3.5 trillion by 2033 at an annual growth rate of 30.6 percent. Even the narrower enterprise AI market, estimated at $107 billion in 2025, offers a target market for a company like Unframe that will not generate any natural ceiling effects for years to come. The crucial factor is not the total market volume, but whether Unframe can demonstrate that its managed delivery model scales both qualitatively and culturally with significantly larger deployment volumes.

The $100 million total revenue in twelve months, the 400 percent net revenue ratio, and the $100 million total capital base are, in this interpretation, not target points, but rather starting points for a far larger economic gamble: that companies are willing to pay for real results rather than theoretical possibilities – and that Unframe is able to consistently deliver on this expectation. If this gamble pays off, Unframe will not just be another successful startup, but a structuring player in a market that is currently discovering its own logic of maturation.

Konrad Wolfenstein

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