Eventual Tackles Data Infrastructure Challenges for Multimodal AI

While working as software engineers in Lyft’s autonomous vehicle division, Eventual founders Sammy Sidhu and Jay Chia identified a critical data infrastructure issue, one poised to grow with the rise of AI.

Autonomous vehicles generate vast amounts of unstructured data, including 3D scans, images, text, and audio. Lyft engineers lacked a unified tool to process these diverse data types simultaneously, forcing them to rely on fragmented open-source solutions that were time-consuming and unreliable.

“We saw brilliant PhDs and industry experts dedicating 80% of their time to infrastructure rather than core application development,” Sidhu, Eventual’s CEO, shared in a recent TechCrunch interview. “Most challenges stemmed from data infrastructure.”

Sidhu and Chia developed an internal multimodal data processing tool at Lyft. When Sidhu explored new job opportunities, interviewers repeatedly expressed interest in a similar solution for their organizations, sparking the idea for Eventual.

Eventual created Daft, a Python-native open-source data processing engine designed to efficiently handle diverse data types, from text to audio and video. Sidhu aims for Daft to revolutionize unstructured data infrastructure, much like SQL transformed tabular data.

Founded in early 2022, nearly a year before ChatGPT’s debut, Eventual launched Daft’s initial open-source version that year and plans to introduce an enterprise product in the third quarter.

“ChatGPT’s rise highlighted the surge in AI applications using varied data types,” Sidhu noted. “As developers incorporated images, documents, and videos, we saw a dramatic increase in Daft’s usage.”

Save Over $200 on Your TechCrunch All Stage Pass

Innovate smarter, scale efficiently, and forge meaningful connections. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and more for a day of strategic insights, workshops, and networking.

Save Over $200 on Your TechCrunch All Stage Pass

Innovate smarter, scale efficiently, and forge meaningful connections. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and more for a day of strategic insights, workshops, and networking.

While Daft originated in the autonomous vehicle sector, its applications span industries like robotics, retail tech, and healthcare. Eventual now serves clients such as Amazon, CloudKitchens, and Together AI.

Eventual secured two funding rounds within eight months: a $7.5 million seed round led by CRV and a $20 million Series A led by Felicis, with participation from Microsoft’s M12 and Citi.

The recent funding will enhance Eventual’s open-source offerings and support the development of a commercial product, enabling customers to build AI applications using processed data.

Astasia Myers, a general partner at Felicis, discovered Eventual through a market analysis focused on data infrastructure for multimodal AI models.

Myers highlighted Eventual’s first-mover advantage in a space likely to become competitive, noting the founders’ direct experience with data processing challenges. She emphasized the growing need for such solutions.

According to MarketsandMarkets, the multimodal AI industry is expected to grow at a 35% compound annual rate from 2023 to 2028.

“Global data creation has surged 1,000x in the past 20 years, with 90% generated in the last two years, and most of it is unstructured, per IDC,” Myers said. “Daft addresses this massive trend in generative AI, which relies on text, images, video, and voice, requiring a multimodal-native data processing engine.”