Eventual’s Daft engine, designed to swiftly handle diverse data types like text, audio, and video, aims to revolutionize unstructured data infrastructure in a manner similar to how SQL transformed tabular datasets in the past. Despite originating from the autonomous vehicle sector, Daft has found applications in various industries like robotics, retail tech, and healthcare, garnering clients like Amazon, CloudKitchens, and Together AI.
Astasia Myers from Felicis highlighted Eventual’s role in supporting the expansion of multimodal AI models, a segment projected to experience significant growth. With an exponential increase in data generation and the predominance of unstructured data, the need for a multimodal-native data processing engine like Daft has become crucial. Eventual’s innovative solution aligns with the macro trend of generative AI applications across various data formats, including text, image, video, and voice.
Recognizing the rising need for flexible data infrastructure in the age of AI, Eventual secured funding from investors such as CRV, Felicis, Microsoft’s M12, and Citi. This influx of capital will enable Eventual to enhance its open source offering and launch a commercial product for developing AI applications based on processed data. With the growing demand for multimodal AI solutions, Eventual’s pioneering approach in this space and their firsthand experience in tackling data processing challenges have positioned them as a key player in the market.
As Sammy Sidhu and Jay Chia worked on Lyft’s self-driving car project, they discovered a challenge with handling the vast amount of unstructured data generated, such as 3D scans, photos, text, and audio. This led them to create a powerful data processing tool called Daft, aimed at efficiently managing different types of data simultaneously. The idea for their company, Eventual, was born from the realization that many professionals were struggling with similar data infrastructure issues while focusing on core applications.
