Who are Unframe?

Unframe is a fast-growing enterprise AI platform that emerged from stealth in April 2025 with $50 million in funding. The company delivers tailored, production-ready AI solutions in days rather than months, using a modular building-block architecture and a managed delivery model. For enterprise IT leaders exploring how to move beyond AI pilots into scalable, operational deployment, Unframe is a vendor worth understanding. This profile covers what Unframe does, who it serves, and how it compares to alternatives in a crowded market.

Who Are Unframe? Founding Story, Leadership, and Funding

Unframe was founded by Shay Levi (CEO), Larissa Schneider (COO), and Adi Azarya (VP of R&D). Levi is a well-credentialled founder: before Unframe he co-founded Noname Security, an API security business that was acquired by Akamai Technologies for $450 million in 2024. That exit gives Unframe's founding team a track record in building and scaling enterprise software businesses, which is relevant context for any IT leader assessing the vendor's long-term credibility.

The company was founded and operated in stealth before publicly launching in April 2025. At the point of launch, it had already achieved millions of dollars in annual recurring revenue (ARR) and established relationships with dozens of large enterprises across multiple geographies. The funding round of $50 million was backed by Bessemer Venture Partners, TLV Partners, Craft Ventures, Third Point Ventures, SentinelOne Ventures, Cerca Partners, and Terra Nova Ventures. The calibre of the investor base reflects both the reputation of the founding team and the broader investor appetite for enterprise AI infrastructure businesses.

Unframe is headquartered in Cupertino, California, with operational offices in Tel Aviv and Berlin. In December 2025, the company announced a further expansion of its leadership team and global footprint, appointing senior leaders in product solutions, product growth, and global partnerships. This rapid headcount and geographic build-out is consistent with a company scaling quickly to serve growing international enterprise demand. For UK and European IT buyers, the Berlin presence is a relevant signal of European market intent.

What Does Unframe Do? The Managed AI Delivery Platform Explained

Unframe describes itself as a managed AI delivery platform. The core proposition is that enterprise AI has a delivery problem: the technology is capable, the use cases are clear, but the path from idea to working solution is routinely blocked by integration complexity, data governance requirements, infrastructure gaps, and the scarcity of specialist AI talent. Most enterprises that have pursued AI projects have accumulated a mix of pilots that stalled, generic chatbot tools that underwhelmed, and custom builds that took far longer and cost far more than anticipated.

Unframe's answer to this problem is a platform built from hundreds of reusable, modular AI building blocks, spanning capabilities including semantic search, reasoning, automation, intelligent agents, and document extraction. These building blocks are orchestrated through what Unframe calls a Blueprint: a specification file that defines how the right components connect together to deliver a complete AI solution for a specific enterprise use case. The Blueprint approach means that Unframe can configure and deploy a tailored solution for a given customer's environment without starting from scratch for each engagement.

The four core solution pillars are Observability and Reporting (AI-driven operational intelligence and anomaly detection), Extraction and Abstraction (converting unstructured content such as documents, emails, and PDFs into structured, AI-ready data), Automation and Agents (multi-step agentic workflows that can handle complex processes end-to-end), and Knowledge On-Demand (making enterprise knowledge searchable and retrievable across systems). Each solution can run on any major large language model without requiring model training or fine-tuning.

A distinctive commercial feature is the outcome-based pricing model. Customers try a fully operational solution before making any financial commitment. There are no restrictions on users, integrations, or queries during the evaluation period. Unframe covers the cost of building the initial solution; the customer only pays once they have seen working results. For risk-conscious IT buyers, this is a notable departure from the upfront licence commitments typical in enterprise software.

Unframe also places significant emphasis on data security and deployment flexibility. Solutions can be hosted on-premise, in a private cloud, or as managed SaaS, and no customer data leaves the enterprise perimeter unless the customer chooses otherwise. This is directly relevant for regulated industries and organisations with strict data residency requirements.

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Who Does Unframe Typically Serve?

Unframe targets large enterprises across multiple sectors. The company's publicly referenced customer base includes organisations in financial services, real estate, logistics, technology distribution, and media. The platform is built for the scale and complexity of large organisations: multiple systems, varied data sources, strict governance requirements, and the need for solutions that work alongside existing enterprise infrastructure rather than replacing it.

In terms of the buyer profile, Unframe's primary stakeholders are likely to be Chief Digital Officers, Chief Information Officers, and heads of digital transformation or enterprise architecture. These are the roles that carry responsibility for turning AI strategy into operational reality, and that feel most acutely the gap between what AI vendors promise and what enterprise deployments typically deliver. The platform is also relevant to Chief Technology Officers in organisations where AI capability is becoming a competitive differentiator rather than a back-office efficiency tool.

The vendor is active across North America, Europe, the Middle East, and Sub-Saharan Africa. For UK and European enterprise buyers, the Berlin office and the Tel Aviv engineering hub are relevant: they suggest a vendor with genuine European operational capacity rather than a US-centric platform with limited local support. Sector fit is broad, but Unframe has publicly demonstrated particular traction in real estate, financial services, logistics, and technology distribution.

Unframe's Key Strengths and Differentiators

The most distinctive feature of Unframe's model is the combination of speed and customisation. The standard claim that AI solutions can take months to deliver is the premise that Unframe directly challenges: the Blueprint architecture and modular building-block library are designed to compress deployment timelines to days. In an environment where many enterprise AI projects have stalled at pilot stage, the ability to demonstrate a working, production-ready solution in a short timeframe is commercially significant.

The outcome-based pricing model is a genuine differentiator in the enterprise software market. It lowers the barrier to evaluation and removes one of the most common objections to AI investment: the risk of paying significant upfront costs for solutions that may not deliver measurable value. For procurement teams and finance functions that are scrutinising AI budgets carefully, this model is likely to be attractive.

Unframe's LLM-agnostic architecture is also worth noting. Because the platform is not tied to a single model provider, customers are not exposed to the pricing, performance, or strategic risks of dependence on any one AI vendor. As the large language model market continues to evolve rapidly, this flexibility has practical value for long-term enterprise deployments.

The security and data residency posture is a fourth area of differentiation. On-premise and private cloud deployment options, combined with a policy of not requiring data to leave the customer's environment, address a genuine concern for enterprise IT buyers in regulated industries. For organisations that have hesitated to adopt AI tools due to data governance concerns, this architecture provides a meaningful reassurance.

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How Does Unframe Compare to Competitors?

Unframe competes across multiple overlapping categories depending on the use case in scope. For observability and operational intelligence, competitors include platforms such as Glean and Microsoft Copilot, which offer search and knowledge retrieval across enterprise systems. For document extraction and data abstraction, vendors such as Hyperscience, Instabase, and Eigen Technologies are active in the space. For agentic automation and workflow orchestration, ServiceNow, UiPath, and a range of newer AI-native workflow platforms are all relevant comparators.

The more direct comparison is arguably with enterprise AI platform vendors that position themselves as broad, multi-use-case delivery platforms rather than point solutions. In this space, Unframe competes with vendors including Writer, Moveworks, and IBM watsonx, all of which offer enterprise AI platforms designed to span multiple departments and use cases. The key differentiators Unframe would emphasise in this comparison are deployment speed, the managed delivery model (where Unframe's own team builds the solution rather than leaving implementation to the customer), and the outcome-based pricing structure.

For enterprise IT buyers, the vendor selection decision in this category requires careful thought about where you want the boundary between platform and service to sit. Unframe's model is more managed than most: you are not buying a platform and building on top of it yourself; you are buying outcomes that Unframe's team delivers using its platform. That distinction matters for internal capability building, vendor dependency assessment, and long-term total cost of ownership. For a structured approach to navigating these trade-offs, Viewpoint Analysis offers a Rapid Technology Selection service designed to take buyers from longlist to shortlist in a defined timeframe.

Unframe Customer Examples

Cushman & Wakefield

Cushman & Wakefield, the global commercial real estate services firm, is one of Unframe's most prominently referenced enterprise customers. The company's Chief Digital and Information Officer has cited Unframe as a central element of their AI+ strategy, using the platform to extract operational insights from complex, fragmented data environments and deliver faster, more informed outcomes for clients.

NZZ (Neue Zurcher Zeitung)

NZZ, the Swiss media group, has used Unframe to support its enterprise AI strategy. Published commentary from the NZZ team describes Unframe as having gone into considerable depth to understand their workflows, and positions the engagement as a foundational component of a longer-term AI transformation programme. The deployment illustrates Unframe's applicability outside the more obvious financial services and technology sectors.

Tarsus Distribution

Tarsus Distribution, a technology distributor operating primarily in Sub-Saharan Africa, deployed Unframe to automate its sales quoting process. The outcome was a reduction in quote turnaround time from 24 hours to seconds. This example is notable because it demonstrates Unframe's applicability to operational workflows that may sit outside the typical AI strategy conversation, and it underlines the speed-to-value proposition that the vendor leads with in its commercial positioning.

Summary: Is Unframe Right for Your Organisation?

Unframe is a well-funded, fast-growing enterprise AI platform with a credible founding team and a distinctive commercial model. Its strengths are deployment speed, a managed delivery approach that reduces the burden on internal teams, flexible data residency and security architecture, and outcome-based pricing that lowers evaluation risk.

The vendor is best suited to large enterprises that have a clear AI use case in scope, have struggled to move from pilot to production with other approaches, and want a partner that will build and deliver the solution rather than hand over a platform and a development toolkit. It is less well suited to organisations that want to build deep in-house AI engineering capability on top of a licensed platform, or to smaller businesses where the enterprise-grade architecture represents more complexity than the use case requires.

As with any enterprise technology investment, independent evaluation matters. Unframe's own positioning and customer references tell one part of the story; reference calls with existing customers in comparable sectors and a structured proof-of-concept process will give you the evidence base to make a well-informed decision.

Next Steps: How Viewpoint Analysis Can Help

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