San Francisco-based startup Altara has secured $7 million in seed funding to address fragmented data challenges in the physical sciences. The round was led by Greylock Partners, with additional investment from Neo, BoxGroup, Liquid 2 Ventures, and Google co-founder Jeff Dean. Founded in 2025 by Harvard alumni Eva Tuecke and Catherine Yeo, Altara aims to build an AI layer that unifies scattered technical information into a single, actionable platform. Companies developing batteries, semiconductors, and medical devices generate massive amounts of data that often remain trapped in spreadsheets and legacy systems. This fragmentation hinders innovation and slows failure analysis. According to co-founder Catherine Yeo, engineers frequently spend weeks or even months manually hunting through sensor logs, temperature records, and historical failure reports to diagnose a single issue, such as a battery cell failure during testing. Altara claims its artificial intelligence drastically accelerates this process, condensing what was traditionally a months-long scavenger hunt into a matter of minutes. Corinne Riley, a partner at Greylock, compares Altara's function in the hardware sector to that of site reliability engineers in the software industry. Just as software companies use AI to monitor system health and pinpoint code changes that cause outages, Altara seeks to serve as the hardware equivalent. Its technology analyzes performance data to determine exactly what caused a physical product to fail, enabling faster resolutions and better product iteration. Unlike competitors such as Periodic Labs and Radical AI, which focus on rebuilding scientific research from the ground up, Altara takes a more capital-efficient approach. Rather than replacing existing research and manufacturing infrastructure, the startup integrates an intelligence layer directly into the current data ecosystems of established firms. This strategy allows Altara to provide immediate value without the high costs and long timelines associated with building new laboratory workflows from scratch. Riley predicts that AI applications in physical sciences represent the next major frontier for technological development. She anticipates a significant surge in innovation within the sector, driven by tools that can unlock the insights hidden within decades of accumulated industrial data. By bridging the gap between raw data and actionable intelligence, Altara positions itself to become a critical utility for the future of hardware engineering and materials science.