Larrenin offers an independent, vendor‑neutral platform that automatically discovers every AI tool, workflow, and agent used across an enterprise, mapping real‑time execution to measure adoption, fluency, and productivity. It provides dashboards, benchmarks, and a conversational “Scout” agent that quantify AI cost, ROI, and compliance, helping leaders enforce governance, eliminate waste, and make data‑driven investment decisions.
Funding
$17M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
GVFounders
Product
Problem
Enterprises invest heavily in AI tools and agents, but lack visibility into actual usage, adoption depth, and financial impact, often relying on spreadsheets or vendor dashboards that provide incomplete or biased data.
Solution
Larridin provides an independent, vendor‑neutral platform that automatically discovers every AI tool, workflow, and agent in use across an organization. It maps real‑time workflow execution, measures AI adoption, fluency, and productivity, and quantifies the cost and ROI of each AI instance. The system surfaces policy compliance, license utilization, and token spend, enabling leaders to enforce governance, eliminate waste, and make data‑driven decisions about AI investments. Insights are delivered through dashboards, benchmarks, and a conversational “Scout” agent that answers natural‑language queries about AI performance and cost.
Target Audience
Primary customers are enterprise leaders responsible for AI governance and performance—including CFOs, CIOs, CISO/CAIOs, CHROs, and business unit heads—who need organization‑wide visibility into AI adoption, cost, and impact.
Features
- Automatic discovery and mapping of AI tools, agents, and workflow transitions via a lightweight telemetry agent and browser extension, requiring no manual workshops
- Real‑time dashboards showing AI adoption rates, fluency scores, and cost per team, role, and application, with built‑in benchmarking against enterprise goals
- Policy enforcement and approval workflows that identify approved vs. shadow AI usage and enforce data‑handling and security rules
- License and token utilization analytics that highlight idle seats, redundant tools, and token‑spend anomalies for immediate cost optimization
- AI impact metrics for engineering (velocity, code quality, AI slop index) and business functions (pipeline creation, deal velocity) that link usage to measurable productivity and revenue outcomes
- Conversational “Scout” agent that provides natural‑language answers about AI usage, spend, and compliance across the organization
- Integration hooks for exporting data to governance frameworks (e.g., EU AI Act, ISO 42001) and existing BI or EHR systems via APIs