San Francisco-based startup Altara has secured $7 million to tackle the fragmented data problem plaguing physical sciences. Its AI system aims to streamline the 'scavenger hunt' of finding failure causes in batteries and semiconductors, condensing weeks into minutes.

The company was founded by Eva Tuecke and Catherine Yeo, both with backgrounds in particle physics research and AI engineering. They believe their technology could revolutionize how scientists approach complex data challenges.

Greylock’s Corinne Riley compares Altara to site reliability engineers (SREs) in the software world; SREs identify issues swiftly by pinpointing changes that led to system failures. Similarly, Altara's AI can diagnose hardware malfunctions more efficiently than manual methods.

While Altara’s approach is less capital-intensive and focuses on integrating into existing systems, other startups like Periodic Labs and Radical AI are also exploring the use of AI in physical sciences. This suggests a growing trend towards leveraging machine learning for scientific research acceleration.

The potential impact is significant – from improving battery longevity to enhancing semiconductor performance, Altara’s technology could pave the way for more efficient R&D cycles across various industries.