While working as software engineers on Lyft’s autonomous vehicle team, Sammy Sidhu and Jay Chia noticed a serious gap in the way data was handled. Self-driving cars produce a staggering amount of unstructured data—from 3D scans to audio, text, and images—but there was no unified platform to process all of it efficiently.
Instead, engineers at Lyft were cobbling together open source tools, spending most of their time on infrastructure rather than building AI models or applications. “We had all these brilliant PhDs spending 80% of their time wrangling infrastructure,” said Sidhu, now CEO of Eventual, in a recent interview. “The biggest pain points were all around multimodal data infrastructure.”
That realization led to the birth of Eventual, a startup building a Python-native, open source data processing engine called Daft. Designed to seamlessly handle various data types—text, audio, video, and more—Daft aims to do for unstructured data what SQL did for tabular datasets.
A First-Mover in the Multimodal AI Race
Eventual began in early 2022, months before the generative AI boom that ChatGPT would trigger. Even then, the founders recognized a critical infrastructure gap. When Sidhu started interviewing for jobs, he found other companies asking him to build something similar to the internal data platform he’d built at Lyft.
So, they launched Daft as an open source tool later that year—and adoption quickly followed. As more developers began integrating images, documents, and video into their AI applications, demand for a robust multimodal-native engine spiked.
Though the initial inspiration came from autonomous vehicles, the use cases for Daft span industries: robotics, healthcare, retail tech, and more. Eventual’s client list already includes Amazon, CloudKitchens, and Together AI, proof that the demand for structured unstructured data is growing fast.
Fueling Growth With Back-to-Back Funding Rounds
To accelerate its mission, Eventual has raised $27.5 million in just eight months. A $7.5 million seed round led by CRV came first, followed by a $20 million Series A led by Felicis, with participation from Microsoft’s M12 and Citi Ventures.
Astasia Myers, General Partner at Felicis, discovered Eventual during a market research push focused on AI data infrastructure. What impressed her most was the team’s first-hand experience and their early lead in a space set to become increasingly competitive.
“The annual volume of data has exploded 1,000x in the last two decades. 90% of it is unstructured—and most of it was generated just in the last two years,” Myers noted. “Daft is purpose-built for this future, where generative AI depends on processing text, image, voice, and video together.”
With the new capital, Eventual plans to expand its open source tools and launch a full commercial platform in Q3. The goal: empower companies to build AI applications using clean, multimodal data from a single engine—without needing to reinvent the wheel.
