SAN FRANCISCO, Calif. - September 1, 2026 - Today, Guickly, the AI measurement layer for enterprises, announced the launch of the company with $4.2M in seed funding, led by Engineering Capital, to monitor and attribute AI investments and spend, offering total visibility into AI expenses and ROI while optimizing AI usage and maintaining the productivity and speed of innovation. Guickly is bringing clarity to the process of measuring AI usage, answering sometimes-opaque questions: How much is AI costing, where is it operating within the organization, who is using it (and who is not), who is tokenmaxxing, and what is AI’s return? 

CXOs, CIOs, IT Directors, and other executives are struggling to get a clear answer, with shadow AI, token sprawl, and siloed data obscuring the true cost of AI initiatives and creating a growing blind spot for leadership. According to McKinsey, only 39% of organizations can attribute any bottom-line impact to AI. Guickly aims to be the layer companies use to identify and measure AI spend and its ROI, controlling and curtailing runaway AI usage without sacrificing performance. 

“Today, organizations are already spending as much on AI as they do on cloud infrastructure, but unlike the cloud, they may have no idea where that money is going,” said Prashant Jalan, founder of Guickly. “When I was leading an Applied AI team at Google and making products for billions of people, I traced where every cycle and every byte was going. But I realized that even the largest tech companies didn’t always know what AI was doing within their walls. So I resolved to fix that.” 

Spanning the AI arc from adoption to control to optimization, Guickly gives time back to AI leaders, showing them every AI tool (including shadow AI), every AI agent, and every dollar spent on AI in one place. It integrates into enterprises’ existing environments. The measurement that Guickly provides also gives leaders the foundation to rein in and get more from every AI spend. Guickly reveals shadow usage, sets budgets per employee and per tool, and flags waste like unused licenses and overpriced models. 

Unlike other solutions, Guickly never transmits sensitive content like prompts, source code, or other confidential company information to its servers. Enterprises’ sensitive data stays on-premises the entire time, making it a practical solution for heavily regulated industries like finance, automotive IoT, and pharmaceuticals. The AI visibility, control, and optimization features of Guickly support deployments across any industry with a substantial number of AI users. 

Guickly offers a solution for enterprise leaders who are struggling to adapt to AI that does not follow the rules of legacy 2010s IT playbooks. Traditional SaaS models are a fixed cost based on per-seat licenses, while AI is more like a metered utility, with a variable utility cost based on consumption. Without a consistent way to monitor AI usage, organizations run the risk of billing surprises and budget overruns. 

During his time as an Applied AI Lead at Google, Jalan built a profiler to optimize TPU performance. When he founded Guickly, he drew upon this technical knowledge, along with experience designing commercial customer-facing features.  

“I’ve been following Prashant’s career trajectory for years and knew he has the education, experience, ambition, and most importantly, the commercial instinct to build an enduring company,” said Ashmeet Sidana, Chief Engineer, Engineering Capital. “Enterprise AI expenses are sky-rocketing and Prashant has built an easy way for enterprises to understand AI usage, financial expenditures, and access across the organization. The first step to enterprises seeing a return on their AI investment is AI accountability and that’s what Guickly provides.”

In addition to Engineering Capital, co-investors include Converge VC, Neon Fund, and a number of smaller angel investors. 

“At Converge VC, we back both exceptional teams and solutions that address critical white-space opportunities,” said Anshu Agarwal, General Partner, Converge VC. “Guickly brings the best of both: Prashant’s deep technical expertise from more than eight years in applied AI at Google, combined with an integrated enterprise AI layer that brings together visibility, control, and optimization. We believe this comprehensive approach is essential for enterprises looking to deploy and manage AI securely and effectively at scale.”

“We’ve been following AI since the beginning and investing in AI solutions and infrastructure almost as long. We backed Prashant and his team because we believe that Guickly will become essential to any enterprise hoping to use AI effectively,” said Siddhartha Ahluwalia, Founder, Neon Fund. “Prashant’s background has enabled him to understand AI usage and how to measure it in a way that few others can. And that knowledge shows in the solution he has built at Guickly. The early design partners and customers concur on the unique value that Guickly brings.”

Built with the input of CIOs and CXOs, Guickly is designed with the values of time, clarity, and speed, providing accurate and comprehensive information as quickly as possible. 

“We believe time is sacred, and speed is respect,” said Jalan. “Leaders shouldn’t have to chase people to find answers. Guickly brings the answers to them without them having to ask.” 

About Guickly

Guickly is the AI measurement layer for enterprises, providing clarity and visibility into AI expenses, usage, access, and ROI. Guickly optimizes AI usage to maintain performance while preventing cost overruns and eliminating token sprawl. It also keeps sensitive data on premises and integrates into existing tech stacks. Guickly’s founder, Prashant Jalan, was previously an Applied AI Lead at Google, where he helped build the Google Maps speed limit feature and developed a profiler to optimize TPU performance.