Kalavai

Cambridge Judge Business School

Industry: AI infrastructure

MBA Founding Student Name(s): Annie Wang, Carlos Fernandez Musoles

Brief Description of Solution: Kalavai is an open-source AI infrastructure platform that makes hosting enterprise AI simple and affordable. We turn any computing device – think desktops and underutilized GPUs— into a scalable AI cloud. Companies can deploy and run AI workflows without complex DevOps or expensive data-center dependence, cutting inference costs by up to 80%. Our data centers partners unlock new revenue from their underutilized hardware and enterprises get fast, secure, and sustainable compute at a fraction of the cost.

Funding Dollars: $200K, raising pre-seed next year. We secured our first VC funding directly from MBA pitch competitions.

What led you to launch this venture? We kept seeing the same problem: companies were hitting walls because AI infrastructure was too expensive, too complex, and too slow to scale. At the same time, millions were being poured into new data centers, while existing devices around the world sat massively underutilized. That disconnect didn’t make sense. The waste, the bottlenecks, and the idea that only well-resourced companies could innovate made it clear the system was broken. Kalavai started from askin g why aren’t we using the compute we already have? That realization, and the frustration behind it, is what pushed us to build Kalavai.

What has been your biggest accomplishment so far with venture? Our biggest accomplishment has been solving the communication problem in a distributed AI setup. Everyone knows distributed systems are “supposed” to be too slow or too inconsistent for AI workloads. We proved that assumption wrong. Based on Carlos’ PhD, we made remote computers work seamlessly together to the point where teams can run real AI workflows across heterogeneous devices without feeling the penalties of distribution. That breakthrough unlocked real adoption from early customers and validated our long-term vision.

What has been the most significant challenge you’ve faced in creating your company and how did you solve it? Our hardest challenge wasn’t technical but conviction. When you’re building something non-traditional in AI infrastructure, people don’t always get it immediately. We heard more “no” than “yes,” and it forces you to question your direction. The way we overcame it was by deeply validating the pain points with real customers, especially those struggling with compute scarcity, unpredictable GPU costs, and DevOps bottlenecks. The more conversations we had, the clearer the demand became. That customer clarity helped us drown out the noise and stick to the long-term strategy.

How has your MBA program helped you further this startup venture? The MBA program didn’t just support our startup, it started it. I met my co-founder in an entrepreneurship class where a single debate about AI infrastructure sparked what became Kalavai. The program gave us immediate access to mentors, pitch competitions, and investors. The feedback loop was incredibly fast; we iterated quickly and closed our first investment during the MBA.

Which MBA class has been most valuable in building your startup and what was the biggest lesson you gained from it? Our Entrepreneurship class and concentration were the most impactful because they forced us to move from theoretical thinking to real-world execution. The class taught us to validate quickly, take feedback seriously, and iterate fast.

What professor made a significant contribution to your plans and why? Professor Simon Stockley. He challenged our assumptions and pushed us to make the idea sharper. He gave us his time, opened doors, and shared the kind of practical wisdom early-stage founders actually need. His belief in us (at a stage when the idea was still raw) gave us the momentum to keep going through the inevitable rollercoaster of building a company.

How has your local startup ecosystem contributed to your venture’s development and success? We started our journey in Cambridge, surrounded by scientific innovation and incredible technical talent from day one. London was just a short train away and added exposure to diverse founders, operators, and investors who challenged us with different perspectives. After the MBA, returning to California connected our UK-based insights with the speed and ambition of the Silicon Valley ecosystem. Operating across both ecosystems gave us the unique insights into innovation in different markets.

What is your long-term goal with your startup? Our long-term goal is to lower the barrier of entry to AI by democratizing access to AI infrastructure globally. AI shouldn’t be something only large tech companies can afford to build with. We want Kalavai to fundamentally shift how AI workloads are run. If we do our job right, any team can build and deploy production grade AI wherever, whenever.

Looking back, what is the biggest lesson you wished you’d known before launching and scaling your venture? I wish I had known how normal it is for people not to understand a new idea at first. When you’re building something that challenges existing infrastructure assumptions, skepticism is the default. That skepticism can trigger your own doubts if you’re not prepared for it. The biggest lesson was learning to hold strong conviction while collecting real evidence from customers to guide our direction.

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