Unframe AI
Verified Oct 15, 2026 ActiveBuilds custom AI agent deployments for large enterprises and charges only once the deployment shows results.
Listed on EffectHub ·
Pricing
- Pricing model
- Contact sales
Pricing is outcome-based and negotiated per deployment; the company states customers pay when a deployment shows measurable value rather than a fixed subscription fee.
Use tool ↗About Unframe AI
Unframe is an enterprise AI platform founded in 2024 by Shay Levi, Larissa Schneider, and Adi Azarya, with Levi previously having co-founded Noname Security. Instead of selling a self-serve product, Unframe builds tailored AI deployments for large companies, an agent orchestrator, a data layer that unifies enterprise systems into AI-ready context, and voice-agent capabilities, aimed at sectors like financial services, insurance, healthcare, and manufacturing. Pricing is outcome-based: the company states customers pay once a deployment demonstrably delivers value rather than a fixed subscription. The company raised $50 million in Series B funding and reported already having millions in annual recurring revenue at launch.
- Platforms
- Web
- API
- Not confirmed
- Last verified
- Oct 15, 2026
Verified sources
- Describes the Agent Orchestrator, Knowledge Fabric, and outcome-based pricing modelunframe.ai ↗
- Independent coverage confirming the founders, funding amount, and Cupertino headquarterssiliconangle.com ↗
Is Unframe AI legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Domain registered
- May 22, 2023 (3 years ago)Registry record (RDAP), registrar NameCheap, Inc., looked up Sep 20, 2026
- TLS certificate
- Issued by Google Trust Services, valid until Jan 5, 2027Seen in our own connection to the site on Oct 15, 2026
- Last checked
- Oct 15, 2026The site answered
- On the market since
- 2024The vendor's own material, read by us
- Operated by
- Unframe, United StatesOur research, card verified Sep 1, 2026
Pros & cons of Unframe AI
Unframe is a fit for a large company that wants a custom AI deployment built for its specific systems rather than a generic product it has to adapt to, and the outcome-based pricing removes some of the risk of paying for something that doesn't work. That same custom, sales-led model means there's no way to try it cheaply or self-serve, every engagement starts with a sales conversation.
Pros
- Outcome-based pricing ties cost to demonstrated value rather than a flat subscription
- Founding team has prior enterprise-security exit experience, a reasonable trust signal for enterprise buyers
- Raised substantial funding specifically to build out enterprise deployments
- Covers agent orchestration, data unification, and voice agents rather than a single narrow feature
Cons
- No self-serve trial or published pricing, everything runs through a sales process
- Deployments are custom, so results and timelines likely vary a lot by engagement
- A young company (founded 2024), so long-term track record is still limited
Best for
- Large enterprises in regulated industries needing a custom AI deployment
- Companies wanting to tie payment to demonstrated results rather than a flat subscription
Not for
- Small or mid-size companies without enterprise-scale budgets
- Anyone wanting to self-serve or trial the product without a sales process
Watch out: Outcome-based pricing sounds low-risk, but get the specific success criteria and payment trigger in writing before starting a deployment, since what counts as delivered value is open to interpretation.
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