Inside Sai Vivek Peddi's Journey Building MLPal: Enabling Enterprises to Create Their Own Agentic Systems
Enterprises across industries are racing to deploy agentic AI solutions. But most use cases never make it to production.
Verticalized agents offered by third-party vendors are great for solving specific workflows, but they leave enterprises trying to stitch together 30-40 separate tools across business functions. Building agentic solutions in-house, on the other hand, often leads to inconsistent results and runaway costs.
After having spent years researching GPU kernels and distributed systems, Sai Vivek Peddi recognized that agentic AI solutions, if deployed in a scalable way, could be the future of enterprise operations. So he set out to build MLPal.
Introducing MLPal: A horizontal platform for building agentic systems
MLPal is a horizontal platform that enables enterprises to build agentic systems for the unique workflows of their business. Instead of forcing teams to choose between brittle vertical agents and costly from-scratch builds, MLPal offers "an agentic system for building agentic systems," which makes it possible to create a variety of specialized agentic solutions without data silos and tech debt.
The platform follows a first principles approach—starting by building the foundational blocks for agentic systems, and then developing builders that use those blocks to assemble agentic solutions. As a result, MLPal can span the full stack for an enterprise, from foundation models (open, closed, and fine-tuned) up to the application layer, supporting both static agents, for repeatable tasks that need high accuracy, and dynamic agents for one-off tasks.
The real advantage of the platform, Vivek describes, is its compounding intelligence. "Every time we onboard an enterprise and they start using the platform, the platform keeps learning about the enterprise. All the interactions—the way you program it, the way you interact with it, the logs the agent runs—are used to create the enterprise's foundation models. It becomes an appreciating asset for the company, not a depreciating one."
Vivek’s journey to MLPal
Vivek grew up in Hyderabad, India, studying relentlessly throughout his childhood to earn a place at BITS Pilani, one of India’s best schools. After graduating, he spent several years working for fintech firms in India, before coming to UC Davis for a Masters in Computer Science.
Working alongside his professor, Dr. Deep Ghoshal, Vivek did research on network systems, going deep on hard problems like GPU compute efficiency and distributed training and inference.
Although Vivek initially took an ML engineering job at JP Morgan after graduating, his world changed when he saw ChatGPT land in 2022. He immediately recognized that the technical research he'd spent years on suddenly had important commercial potential. He reconnected with his professor, Dr. Ghoshal, who looped in serial founder Prem Jain, and the team set out to build MLPal.
Having launched just 6 months ago, MLPal already has three enterprise pilots underway and is planning its seed round for the end of July 2026.
Vivek’s advice for fellow founders
As he navigates the ups and downs of the founder's journey, Vivek often references the words of Winston Churchill—"Success is not final, failure is not fatal: it is the courage to continue that counts." Every founder faces a different, unpredictable set of challenges, he explains. “You can't always be successful. Failure is part of the process—it's just about continuing on that path. It comes with a lot of pain, but you have to be tolerant towards it. So just keep going."
But at the same time, Vivek believes it’s important to be open to things that you haven’t planned for. “Things you think will happen might not, and things you don't expect might.”
To learn more about Vivek’s founder journey, you can follow him on LinkedIn and visit MLPal.ai.
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