Uvi's $7.5M Bet on AI Agent Business Context

Agentic AI startup Uvi has raised $7.5 million in seed funding to solve one of the quietest drains on enterprise AI budgets: every new AI agent starts with zero AI agent business context — no knowledge of the company's processes, operations or history — and relearning it over and over costs a fortune in tokens, according to SiliconANGLE.

The round was led by CT Innovations, with participation from Cardumen, Mana VC and 91 Ventures. Uvi, officially known as Naboo Research Ltd., sells what co-founder and chief executive Gilad Salinger calls a "context reasoning layer" — infrastructure that gives every AI agent in a company access to a shared institutional memory instead of forcing each one to reconstruct it from scratch.

The context problem costing enterprises eight times too much

Each time a new AI agent is deployed, it knows nothing at all about the company that deployed it. Those agents have to learn everything about the company's business processes and operations from scratch in a process that is repeated again and again, racking up significant costs in computing resources — rebuilding AI agent business context every single time, Salinger told SiliconANGLE. Uvi's own benchmark data suggests internally built agents cost the average company roughly eight times more than they should because of that repeated context-gathering, according to the report.

The problem is getting worse as adoption accelerates. Salinger said the industry's early anxieties were about agent accuracy and action safety; the conversation has now shifted to cost, tokens and speed. His diagnosis is structural rather than a model-intelligence issue: large companies run dozens of tools with completely different data shapes, and no agent can yet pull a slice of data from Snowflake and another from GitHub in parallel and reason across both. So an agent that completed a task yesterday can fail the identical task today, because it never retained how the work actually gets done, he explained.

The unit economics of AI agent business context are stark. Without a context layer, agent responses cost between $0.75 and $1.41 each, sometimes exceeding $2, according to Uvi's benchmarks reported by SiliconANGLE. Routed through Uvi, that falls to an average of 10 to 16 cents per answer. For open-ended questions — where agents can get stuck in recurring search loops, relearning the same context repeatedly to answer one prompt — Uvi claims a 46x cost reduction while answering twice as fast.

A knowledge graph with permissions built in

Uvi's answer is a company-owned knowledge graph that functions as an infrastructure layer sitting underneath every AI agent the company deploys. The system links fragmented enterprise systems — Slack, Jira, Confluence, Salesforce, code repositories, even minutes of internal meetings — and builds a map showing how the information connects, reconstructing the full AI agent business context behind each decision: not just what it was, but why it was made and what impact it had, according to SiliconANGLE's coverage.

Two design choices stand out for anyone running agents at scale. First, a custom semantic layer on the knowledge graph learns the organization's engineering stack, dependencies and company-specific jargon, so agents interpret internal terms accurately rather than guessing. Second — and rarer — the layer is permission-aware by design. Uvi built an access control model with data tagging that operates at the application and administration layers, so, as Salinger put it, knowledge-graph data carries its permissions with it and agents cannot surface information from tools or employees that are no longer there. Most retrieval-augmented pipelines ingest once and forget who was allowed to see what; Uvi reconstructs relationships across platforms and keeps the access context attached. That permission-carrying design is what makes shared AI agent business context deployable in regulated enterprises.

Early traction and the bigger bet

Uvi has already won over companies including Monday.com, Lemonade and Melio. At Monday.com, the platform helps agents understand the relationships among Jira tickets, pull requests and feature flags — without that context, agents return disjointed ticket lists that humans have to sort through manually, Salinger said.

The investor thesis is blunt: CT Innovations' Gwen Cheni told SiliconANGLE that virtually every company she speaks with about AI agents has struggled to build context at scale, and that Uvi is the finished version of an AI agent business context project those companies have already paid for multiple times over.

Salinger's longer-term bet: within three years, the enterprise AI stack will not be organized around chat interfaces or document search but around a persistent context layer that every agent, model and identity plugs into. Companies with that AI agent business context will ship new agents the way they ship code through CI/CD pipelines, while those without it keep paying models to relearn their business every morning, he predicted.

For the agent ecosystem, the raise is a signal that AI agent business context is becoming the unit-economics story of the agentic era, not just a technical nicety. Accuracy got agents into the enterprise; cost structure will decide which ones stay. Infrastructure plays that cut the token bill — context layers, gateway-side policy enforcement (a trend rival Bigeye pushed further the same day with runtime enforcement through AI gateways, according to EIN Presswire), shared memory — are becoming the picks-and-shovels layer, alongside the services-led agent engineering rollouts and agentic IT operations other enterprise players are pursuing. Uvi's seed is another bet that the winners will be the ones who make every agent fluent in your business the moment it boots up.