Unframe Secures $50M to Revolutionize Enterprise AI Solutions

Apr 10, 2025
Unframe, an AI platform for enterprises, has emerged from stealth with $50 million in funding, aiming to transform AI adoption with rapid, tailored solutions.

Unframe, an AI platform designed to accelerate enterprise AI adoption, has emerged from stealth with $50 million in funding, announced in a press release. The funding round was led by notable investors including Bessemer Venture Partners, TLV Partners, Craft Ventures, Third Point Ventures, SentinelOne Ventures, and Cerca Partners.

Unframe's platform offers a turnkey solution for enterprises, enabling them to deploy customized AI solutions in hours rather than months. This approach aims to replace legacy software systems with more efficient, tailored AI solutions. The company has already secured partnerships with several large enterprises globally, achieving millions in annual recurring revenue.

Founded by Shay Levi, who previously co-founded Noname Security, Unframe is led by a team with extensive experience in scaling businesses and developing enterprise-grade AI solutions. The platform is designed to integrate seamlessly with existing enterprise systems, ensuring that customer data remains secure within their environments. Unframe's business model allows customers to experience the impact of AI solutions before committing financially, making AI implementation risk-free.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like AI Funding Brief, Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

AI Funding News Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.