AI/ML
Israeli startup Uvi says enterprises enterprises are paying frontier AI companies to repeatedly re-learn what their own businesses already know but cannot surface. It can reveal this hidden context using knowledge graph technology.
Uvi says every AI agent an enterprise deploys starts work knowing nothing about the company it works for. It has to reconstruct that context from scratch, and the company pays a frontier model every time it does. It has knowledge graph technology to give Claude, Gemini, GPT and every other AI tool a company uses the missing context behind its decisions, conversations and code, creating a context reasoning layer the company owns once and can make available to every agent it runs.
CEO and co-founder Gilad Salinger said: "Enterprise context is a problem that gets bigger as AI adoption grows. Early use cases exposed accuracy gaps. Today, the conversation has shifted to cost and tokens. But as companies move into more complex agentic workloads, the real bottleneck becomes speed. And speed depends on context: understanding what the organization knows, what other agents are changing in parallel, and the history behind those decisions, then narrowing it down to exactly what each agent needs to keep moving."
Keeping a semantic layer, a knowledge graph and the reasoning layer on top relevant, permissioned and in sync with live data is the heavy lifting no engineering team wants to own. Uvi says it does it for them.
Salinger and CTO Dror Wolmer, both in Tel-Aviv, founded Uvi in 2023 to build the substrate enterprise AI agents need to act inside organizations, not just summarize documents. It was initially called Naboo. Salinger is an ex-VP technology at Nutrifresh Services, which acquired Fetchy, a company he founded, while Wolmer is an ex-security researcher and CTO of Cognigo, which was acquired by NetApp.
Naboo said it was building a “Reasoning Layer” (also called an enterprise context or semantic layer) for AI agents so they can act inside organizations rather than just summarize documents. Enterprise AI agents often fail or become expensive because organizational knowledge is scattered across systems (code, tickets, PRs, Slack, docs, monitoring) and the why behind decisions is rarely explicitly recorded. Naboo/Uvi addresses this by constructing a Decision Graph.
- Decisions are modeled as first-class nodes (with owner, trigger, blockers, dependencies, and supporting evidence).
- It uses live joins/ETL across tools (GitHub/GitLab, Jira/Linear, Slack, Confluence/Notion, Datadog/Splunk, internal services, etc.).
- Agents query it via GraphQL or MCP (Model Context Protocol) and receive a structured chain of relevant decisions instead of a list of documents or similar chunks.
- It runs on-prem or in the customer’s virtual private cloud (VPC), with native RBAC/permissions enforced at retrieval and zero data egress.
This is positioned as more than RAG (Retrieval-Augmented Generation) , enterprise search (e.g., Glean-style), or a standard knowledge graph—focused on intent, company-specific language/semantics, and actionable reasoning chains.
Uvi says RAG was a breakthrough for giving LLMs access to external knowledge. It works by embedding documents into vectors, then retrieving the most semantically similar chunks at query time. For a customer-support bot searching a knowledge base, this is effective.
But enterprise R&D environments aren't knowledge bases. They're living systems with code repositories, ticket trackers, pull requests, Slack conversations, monitoring dashboards, CI/CD pipelines, and documentation - all interconnected, constantly changing, and governed by strict access controls.
When an AI agent needs to help a developer with a task, it doesn't need “the 10 most similar document chunks.” It needs to understand the specific context of that task: which code is involved, who owns it, what decisions were made, what changed recently, and what the developer is allowed to see. RAG can't provide this. A Reasoning Layer built on a Decision Graph can.
Uvi runs in production at Lemonade and monday.com, both public companies, and at Melio, now part of Xero. It connects to Slack, Codebase, Coding Sessions, Meeting Summaries, Jira, Confluence, and more, with additional integrations in development. Employees see only what their existing access rules allow, and every answer traces back to its source.
The company says that, at monday.com, asking what shipped last week does not lead to a single record. A feature may exist across a ticket, a pull request and a feature flag that only make sense together. Without understanding those relationships, an AI agent can return a list of tickets and still fail to answer the question.
A Uvi blog discusses a benchmark test around questions such as: "Do we have a guide for creating a GitHub app?” It ran the same set of questions two ways: once through the context layer, once through a do-it-yourself agent over the same sources, and measured cost per correct answer, total model spend divided by the questions actually answered right, not just answered. The do-it-yourself side came out about 5x more expensive per correct answer, because it burned tokens wandering for answers a good map would have handed it directly.
It says that, built the way most enterprises are building today, an AI answer costs $0.75 to $1.41 and can exceed $2.00. Built on Uvi, the same answer costs just $0.10 to $0.16. On open ended questions where agents became stuck in search loops, Uvi was up to 46 times cheaper.
Uvi is model-agnostic, saying that AI models can change but a company’s context does not. Companies can change models and agents without forcing each new system to relearn how the business works.
The company has now raised a $7.5 million seed round led by CT Innovations with participation from Cardumen Capital, Manu VC and 91 Ventures. Uvi will use the funding to build its U.S. team, expand its go to market efforts and continue product development.
Find out more about Uvi’s technology here.
Bootnote
Wolmer is the author of three US patents, eg; US-12468695-B1 · Issued Nov 11, 2025: "System and method for querying knowledgebases utilizing a language model based on consolidated identity context." This uses knowledge graph technology and natural language input to traverse a graph and find a context.
Several other companies are exploring the idea of an enterprise having organizational memory, context for AI agents and models, such as HYCU, Jedify, and MinIO.