How Naboo Saves Cost
Five places Naboo's Reasoning Layer cuts cost in enterprise AI deployments. Watch the short explainer, then read the breakdown.
Naboo explainer.
The thesis in one paragraph
Naboo'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.
The token-cost thesis2.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 Naboo 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.
Global-e case study4.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. Naboo cuts per-query token volume so the cap stops binding before it has to be lifted.
The founder confession
"Guys, we're losing too much money on OpenAI - $8,000 today alone. I need visibility on the money spent ASAP."
We built Naboo's cost discipline tooling the next sprint. The point: cost-cap pain is universal, even at the companies building the cost-cap tooling. Precision is the durable answer.
FAQ
What ROI do customers actually see?
Variable by workload. The Global-e benchmark numbers are public: agents grounded in Naboo'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. Naboo'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 Naboo not save cost?
If your workload is mostly conversational support over a static knowledge base (FAQ-style), RAG with caching is cheaper. Naboo's cost advantage compounds with workload complexity - the more systems an agent needs to join across, the more speculative retrieval Naboo eliminates. For low-complexity workloads, the simpler tool wins.
How does Naboo 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.
Related reading
Reasoning Layer for Enterprise AI Agents
Definition, architecture, and the two tiers - Topic Graph and Decision Graph.
Read moreDefinitionWhat is a Decision Graph for AI Agents?
Decisions as first-class nodes - owners, triggers, blockers, evidence. The primitive AI agents need to act.
Read moreHow-toHow to Build a Decision Graph
Seven concrete steps from elicitation to a queryable graph. Two to four weeks via Forward Deployed Agent.
Read moreCFO briefHow to Reduce LLM Token Costs
Don't meter the waste, cut the cause. Reasoning Layer vs observability and caching, compared.
Read moreGuideImprove AI Agent Accuracy
Accuracy is upstream of evals. Four causes of enterprise AI inaccuracy and how a Reasoning Layer fixes them.
Read moreArchitectureConnect Enterprise Data Sources
Live joins vs stale copies. Warehouse, ETL, knowledge graphs, and Reasoning Layer compared.
Read moreGuideOvercome GenAI Hallucinations
Hallucinations are a context-handoff problem, not a model problem. Four causes, one upstream fix.
Read moreHubCompare Naboo
Every category enterprise AI buyers weigh against the Reasoning Layer - in one place.
Read moreComparisonNaboo vs Helicone
Reasoning Layer cuts the cause; Helicone measures the waste. Composable.
Read moreComparisonNaboo vs Langfuse
Different layers. Langfuse versions + traces; Naboo grounds the agent.
Read moreComparisonNaboo vs LlamaIndex
RAG framework vs Reasoning Layer. When to use each.
Read moreComparisonNaboo vs LangChain
Orchestration vs substrate. Compose them.
Read moreBackgroundWhy retrieval was the wrong foundation
How enterprise AI agents got built on RAG, why it falls short, and what a reasoning layer fixes.
Read moreComparisonNaboo vs RAG
Retrieval vs reasoning - head-to-head benchmarks, architecture, and when to use each.
Read moreComparisonNaboo vs Glean
Enterprise search vs reasoning layer - when each fits.
Read moreConceptAI Search vs Reasoning Layer
Search returns links; the reasoning layer returns the chain. When to use which.
Read moreCase studyGlobal-E case study
How Global-E (NASDAQ: GLBE) gave AI agents secure access to customer data.
Read moreComparisonCompare alternatives
Naboo vs other enterprise AI agent infrastructure platforms.
Read moreTalk to us about your AI cost story
Twenty-minute conversation. We'll walk through the specific places Naboo cuts cost in your deployment and what it would take to ship the Decision Graph in 2-4 weeks.