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Eventual launches with $30M in funding to streamline multimodal data processing
- Multimodal data processing startup Eventual Inc. secured $30 million funding for its open-source processing engine, Daft.
- The funding included $20 million in Series A led by Astasia Myers and $10 million in seed funding, significantly backed by Brittany Walker.
- Eventual aims to be the foundational infrastructure for handling unstructured data, supporting various data types like images, videos, audio files, and more.
- Daft is already in use by companies like Amazon Web Services, Essential AI Labs, and Together Computer for AI models, autonomous vehicles, and more.
- Co-founders Sammy Sidhu and Jay Chia developed Daft during their time at Lyft to efficiently process messy multimodal data, such as 3D scans and text.
- Eventual has opened access to its production-grade Daft platform for multimodal AI applications.
- The platform allows for seamless processing of large volumes of unstructured data, enabling AI teams to deploy new features faster.
- With the recent funding, Eventual plans to expand its engineering team and attract talent specializing in distributed systems and product engineering.
- Eventual envisions Daft becoming as impactful for unstructured data as SQL is for tabular datasets, catering to the challenges faced by AI developers.
- The company aims to simplify the processing of multimodal data, helping AI teams save time and focus more on innovation and feature development.
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