Lanai
Lanai is an enterprise AI performance management platform that turns AI activity into operating decisions by showing leaders what AI work is being done, what it costs, what it produces, and what to do next across all models, copilots, agents, and applications. The platform maps token spend to actual work performed and provides workflow-level attribution, enabling board-ready AI ROI reporting without manual assembly.
- Artificial Intelligence
- Data & Analytics
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Enterprises deploying AI across multiple models, copilots, agents, and applications lack visibility into what that AI activity actually costs, what work it performs, and what value it generates. Leaders are forced to audit token usage manually and cannot produce board-ready ROI figures or make informed operating decisions about AI investments.
Solution
Lanai provides an enterprise AI performance management platform that connects AI spend directly to the work performed Morgenthau, its unit economics, and recommended next actions. The platform delivers a unified view of AI activity across every model, copilot, agent, and application, allowing leaders to see exactly what work AI is doing)Skip, what it costs, and what it produces. Lanai maps token expenditures to the specific work pursued and value gained, eliminating the need for manual assembly of ROI reports. It also offers workflow-level attribution, enabling teams to isolate which variables drove a particular result and then scale those successful patterns across the organization.
Target Audience
Primary customers are CTOs, AI leaders, and finance executives at mid-to-large enterprises that have deployed AI tools at scale and need quantifiable ROI, cost visibility, and operational guidance for their AI portfolios.
Features
- Unified dashboard showing AI activity across all models, copilots, agents, and applications with no assembly required
- Token spend tracking mapped directly to work pursued and value gained per function
- Workflow-level attribution that isolates the drivers behind specific results for scaling
- Unit economics analysis for AI operations, connecting costs to outcomes
- Board-ready AI ROI reporting across every organizational function
- Operational recommendations indicating what to change next based on performance data