Where is your company's AI spend going? Jim's building the answer

AI spend is exploding inside every company, but almost no one can see where the money’s going or whether it’s working. Employees get handed ChatGPT, Copilot, Claude Code, and dozens of other tools, and what typically happens is a small slice of the workforce becomes wildly effective with them while the vast majority never gets past surface-level use. Meanwhile, engineering teams are burning through tokens at a rate that can exceed salary costs, and there’s no way for a CFO or CTO to know if that spend is justified, wasteful, or should be reallocated. Our bet at Larridin is drawn from our experience building comScore, is that whenever a large wave of spending moves into a new category, a third-party measurement and audit layer inevitably gets built around it — and no one has built that layer for AI yet.

What Larridin actually does is stack three layers of visibility on top of each other. The base layer tracks adoption and usage: which tools employees are using, how often, and how sophisticated their use is — scored in real time by a lightweight browser extension and desktop agent that reads signals (not actual prompt content or keystrokes) so no sensitive data ever leaves the device. On top of that sits workflow intelligence, which reconstructs how work actually gets done step-by-step — without any pre-built process maps — to surface friction points, bottlenecks, and automation opportunities, then lets you see what separates your most productive people from everyone else. The third layer is developer productivity and spend/FinOps governance: engineer-by-engineer, model-by-model token tracking tied into GitHub and similar systems, so leadership can see whether AI coding spend is translating into real output and can set budgets and alerts around it.

Customers run from ~150-person companies up to enterprises with tens of thousands of employees, with private equity firms increasingly using Larridin to benchmark AI usage and spend across their entire portfolios rather than company by company. Our continued focus on driving value through the enterprise is to ensure the workflow insights and analytics can drive the strategic transformation as well as the tactical at the team and employee level. We recently closed a $30M Series A (on top of a $17M seed from a16z, Google’s Gradient fund, and Bloomberg) to build out this roadmap.