Uvi raises $7.5M for business-context AI agent layer

The problem Uvi is addressing is straightforward: each time a company deploys a new AI agent, the agent has no prior knowledge and must reconstruct the company's processes from scratch. Uvi's co-founder and CEO Gilad Salinger describes this as a structural issue rather than an intelligence one. Large companies run dozens of tools with different data shapes, and there is no existing way for an agent to pull data from, say, Snowflake and GitHub in parallel and reason over both.

Uvi's answer is a "context reasoning layer" built on a knowledge graph. It links fragmented systems such as Slack, Jira, Confluence and Salesforce with other data from code repositories and internal meeting minutes, then maps how the information connects. The layer also uses entity mapping and a custom semantic layer so agents learn company-specific jargon, and it carries an access control model so permissions travel with the data, which Salinger says is where most RAG pipelines fall short.

The cost gap Uvi cites is the crux of its pitch to investors. Uvi's benchmarks show agents that route through its layer can cost 46 times less than non-Uvi counterparts on open-ended questions, while answering twice as fast. CT Innovation's Gwen Cheni says most companies she talks to are still far from building context at scale, and calls Uvi the finished version of a project enterprises have already paid for multiple times over.