How Uvi Saves Cost
Five places Uvi's Reasoning Layer cuts cost in enterprise AI deployments. Watch the short explainer, then read the breakdown.
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Uvi explainer.
The thesis in one paragraph
Uvi's cost story is not a single multiplier. It's a pattern: every place enterprise AI agents waste time, tokens, or attention is downstream of one root cause - the agent doesn't have the right context. The Reasoning Layer hands the agent the chain of decisions, owners, blockers, and supporting evidence on the first query, and the waste compounds in reverse. Below: the five places customers consistently see the spend drop.
The five savings
1.LLM token spend
One structured query against the Decision Graph replaces dozens of speculative RAG retrievals. The agent gets the right context the first time and stops grinding tokens looking for it. Token volume drops at the source - not by metering, but by precision.
2.Engineering research time
Engineers stop spelunking across repos, PRs, tickets, and Slack threads to reconstruct who owns what and what blocks shipping. The agent traverses the Decision Graph and returns the chain - in seconds, not hours. Hadar Geshuny (Sr. Director, Platform Eng) calls this 'significantly reduced engineering research time.'
3.Support resolution time
In the Global-e (NASDAQ: GLBE) deployment, support ticket resolution dropped by 20% after Uvi rollout. Engineers and CX staff stopped paging senior leads to look up state that was already in the systems - the Decision Graph put it one query away.
4.New-hire ramp
Onboarding a new engineer is, mostly, a multi-month context-acquisition project. A Decision Graph compresses the implicit knowledge - who owns what, what triggered what, what blocks what - into a queryable surface a new hire can ask questions of from day one. Global-e reports faster onboarding with less time pulled from senior employees.
5.Budget-cap productivity tax
When procurement caps Claude / GPT spend per seat, engineers ration their AI use and pilots stall. The cost showing up in finance reports is the AI bill - but the bigger cost is the productivity engineers lose to rationing. Uvi cuts per-query token volume so the cap stops binding before it has to be lifted.
The founder confession
“Guys, we’re burning too much money on OpenAI - $8,000 today alone. We need to cut this spend ASAP.”
The company building the precision layer for enterprise AI burned $8K of OpenAI in a single day. Our agents were doing exactly what every enterprise team’s agents do - paying the LLM to guess at context nobody had handed them. We pointed Uvi at ourselves the next sprint. The bill came back to earth.
If we feel it, our customers feel it 10x.
FAQ
What ROI do customers actually see?
Variable by workload. The Global-e benchmark numbers are public: agents grounded in Uvi's Decision Graph won 97 of 100 head-to-head queries against MCP-enabled GPT-4.1, 500+ active users adopted across the engineering org, support ticket resolution -20%, and faster onboarding for new hires. We don't publish a single ROI multiplier because the variance across customers is too high to be defensible - the pattern is consistent, the numbers are workload-specific.
How does the cost compare to building this in-house?
Building a Decision Graph in-house is a multi-quarter platform program: ETL plumbing across every source system, identity resolution, permission mirroring, typed entity definitions, GraphQL surface, MCP server, and ongoing maintenance as the organization's vocabulary evolves. Uvi's Forward Deployed Agent ships the whole thing in 2-4 weeks. The financial comparison is not really platform vs license; it's platform vs faster time-to-value.
Where does Uvi not save cost?
If your workload is mostly conversational support over a static knowledge base (FAQ-style), RAG with caching is cheaper. Uvi's cost advantage compounds with workload complexity - the more systems an agent needs to join across, the more speculative retrieval Uvi eliminates. For low-complexity workloads, the simpler tool wins.
How does Uvi pricing work?
Enterprise contracts only - no SaaS tier. Pricing is structured around the Forward Deployed Agent engagement and ongoing platform license. We don't publish public pricing because deployment scope (number of systems, depth of decision definitions, security requirements) drives the right number. A 20-minute conversation gets you a defensible quote.
How long until we see the cost savings?
Token savings kick in the day the Decision Graph goes live (typically end of week three). Engineering research-time savings are visible within the first month as agent adoption spreads. Support resolution savings show up within the first quarter as CX teams adopt the agent surface. New-hire ramp savings show up with the next cohort of hires.
Talk to us about your AI cost story
Twenty-minute conversation. We'll walk through the specific places Uvi cuts cost in your deployment and what it would take to ship the Decision Graph in 2-4 weeks.