Unframe
by Unframe
Managed AI delivery that turns critical enterprise operations AI-native in days
Unframe is a managed AI transformation platform for large enterprises: its own team builds, deploys and operates production AI solutions inside the customer's perimeter, typically in days rather than months. It targets organisations whose AI pilots keep stalling before production, and it charges only once a working solution has demonstrated measurable business value.
Unframe is a managed AI transformation platform that builds and runs production AI solutions on behalf of large enterprises, rather than handing them a toolkit and leaving implementation to internal teams. The platform is assembled from four parts: an Agent Orchestrator that coordinates multi-step execution with built-in guardrails and observability, a Knowledge Fabric that converts fragmented enterprise data into AI-ready context, an AI-native data store that stays continuously synchronised across systems, applications and APIs, and modular Building Blocks composed into use cases through specification files the company calls Blueprints. Solutions are model-agnostic, running on any major foundation model with no training or fine-tuning required, and deploy inside the customer's own perimeter as a private VPC or fully on-premises, with federated data isolation, policy enforcement, audit logging and zero retention; Unframe publishes SOC 2 Type II, ISO 27001, ISO 42001 and HIPAA compliance alongside regional hosting options. The commercial model is the unusual part: customers operate a fully working solution before paying anything, and only convert to a flat annual fee covering delivery, updates and uptime once value is demonstrated. Founded in 2024 by Noname Security alumni Shay Levi, Larissa Schneider and Adi Azarya, with headquarters in Cupertino and offices in Tel Aviv and Berlin, the company emerged from stealth in April 2025 with $50M raised, crossed $100M in total contract value within twelve months on three-to-five-year deals, and raised a $50M Series B led by Highland Europe in 2026 for $100M total. It concentrates on financial services, insurance, healthcare, manufacturing, real estate and retail, and sits against Glean, Microsoft Copilot, Writer, Moveworks and IBM watsonx depending on the use case.
The CIO or Chief Data Officer at a large regulated enterprise that has defined AI use cases, cannot get them past pilot stage, and has no internal AI engineering bench to build them.
A working, production-grade AI solution running inside your own perimeter in days, with no licence spend until it has demonstrably delivered value.
At a Glance
- Category
- Enterprise Platform
- Pricing
- Contact for pricing, Subscription
- Target Market
- CIOs, CTOs, Chief Data Officers, Enterprise Architects, Heads of Operations
- Deployment
- Hybrid, Self-hosted, Cloud-first
- Founded
- 2024
- Headquarters
- Cupertino, United States
- Team Size
- 51-200
- Customers
- Dozens of large enterprises; publicly named customers include Cushman & Wakefield, NZZ, Climb Global Solutions and AST
Key Features
- ✓Agent Orchestrator
Coordinates multi-step agent execution with built-in guardrails and observability so production runs stay auditable.
- ✓Knowledge Fabric
Turns fragmented enterprise documents and records into AI-ready context that every solution reuses across use cases.
- ✓AI-native data store
Continuously synchronises data across SaaS tools, APIs, databases and files into one queryable foundation.
- ✓Building Blocks and Blueprints
Reusable modular components composed via specification files, so each new use case assembles rather than rebuilds from scratch.
- ✓Model-agnostic execution
Runs on any major foundation model with no training or fine-tuning, so a model swap does not mean a rebuild.
- ✓In-perimeter deployment
Deploys to a private VPC or fully on-premises with federated data isolation, audit logging and zero retention.
- ✓Managed delivery team
Unframe builds and operates each solution itself instead of handing the customer an implementation project.
Capabilities
Use Cases
- •Operational intelligence
Surface answers across scattered operational systems so analysts stop manually reconciling reports between disconnected tools.
- •Document extraction
Pull structured fields from contracts, claims and statements at volume, replacing manual keying and line-by-line review.
- •Agentic workflow automation
Run multi-step back-office processes end to end with guardrails, escalating only genuine exceptions to staff.
- •Enterprise knowledge retrieval
Give staff grounded answers from internal content without any of it leaving the company perimeter.
- •Regulated-industry AI rollout
Deploy AI inside a bank or insurer's own environment where residency rules forbid external SaaS processing.
Ideal For
Best For
- ✓Large enterprises whose AI pilots have repeatedly stalled before reaching production
- ✓Regulated financial services, insurance and healthcare firms that must keep data inside their own perimeter
- ✓CIOs who want a fixed annual cost after a proven outcome rather than an upfront licence commitment
- ✓Teams with clearly defined use cases but no internal AI engineering capacity to build them
- ✓Multi-use-case AI programmes that need one shared data and context layer instead of a separate stack per project
Not Ideal For
- ✗Organisations whose actual goal is building deep in-house AI engineering capability — the managed delivery model deliberately keeps the build with Unframe's team, so the customer accumulates a vendor relationship rather than a skill set
- ✗Small and mid-sized businesses, where the enterprise architecture represents more complexity than the use case requires
- ✗Procurement processes that need published list pricing to benchmark against, since nothing is priced on the website
- ✗Buyers who want to self-serve, trial and evaluate the product without entering a sales-led engagement
Deployment
Market & Ratings
Dozens of large enterprises; publicly named customers include Cushman & Wakefield, NZZ, Climb Global Solutions and AST
Market Analysis
Pros
- ✓Managed delivery removes the implementation project that stalls most enterprise AI platform purchases
- ✓Outcome-based commercial model lets a buyer see a working solution before committing budget
- ✓Real deployment flexibility — private VPC or fully on-premises — backed by SOC 2 Type II, ISO 27001, ISO 42001 and HIPAA
- ✓Model-agnostic with no fine-tuning, so swapping foundation models does not require rebuilding solutions
- ✓Commercial traction is unusually concrete: $100M total contract value on multi-year deals within twelve months of launch
Cons
- ✗No public practitioner signal to check the vendor's claims against — no reviews readable on G2, Capterra or TrustRadius, and the only Hacker News submission about the company drew one point and zero comments
- ✗Analyst coverage flags vendor dependency: because Unframe's team builds and operates the solution, the customer builds no internal AI capability, and the same analysis calls it a poor fit for organisations that want to develop one
- ✗No published pricing of any kind, so total cost of ownership cannot be modelled before entering a sales process
- ✗The headline impact metrics on the site (40% lower storage and retrieval cost, 30% fewer supply-driven stock-outs) are attributed to unnamed customers with no published methodology
- ✗Founded in 2024 and around 130 people, which is a young vendor to place at the centre of a three-to-five-year mission-critical programme
Pricing
Outcome-based enterprise engagement
Contact for pricing
- ✓Fully operational solution delivered before any payment
- ✓Flat annual cost covering delivery, updates and uptime
- ✓No stated limits on users, queries or integrations
- ✓Typical contract term of three to five years
- ✓Deployment to private VPC, on-premises or managed SaaS
No list pricing is published anywhere on the site. Unframe runs an outcome-based model in which the customer operates a fully working solution first and pays only once measurable value is demonstrated, then converts to a flat annual fee covering delivery, updates and uptime; reported contracts run three to five years. Everything is quoted through sales, so cost cannot be benchmarked before an engagement.
Security & Compliance
Connect
Sources
This page was written from 7 sources, 4 on domains other than unframe.ai.
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