San Francisco-based startup Altara has raised $7 million in seed funding to address the data fragmentation slowing progress in the physical sciences, according to TechCrunch.
The company is building an AI layer designed to bridge gaps between disparate data sources, pulling technical information from spreadsheets and legacy systems into a single platform. The funding round was led by Greylock, with participation from Neo, BoxGroup, Liquid 2 Ventures, and Jeff Dean.
Altara targets industries such as battery manufacturing, semiconductor production, and medical device development. These sectors generate massive amounts of data that often remain siloed, making it difficult for engineers to diagnose product failures or improve R&D efficiency.
Automating the R&D 'scavenger hunt'
Co-founder Catherine Yeo, a former AI engineer at Warp, described the current manual process as a taxing investigation. She noted that when a battery fails during cell testing, engineers must manually cross-check various sources, including sensor logs, temperature data, moisture levels, and historical failure reports.
“Scientists and engineers often spend weeks or months on this ‘scavenger hunt’ across a multitude of data sources just to diagnose and resolve failures,” Yeo told TechCrunch.
Altara claims its AI technology can condense this weeks-long manual triaging process into just minutes.
The startup was founded in 2025 by Yeo and Eva Tuecke, a former researcher at Fermilab and SpaceX. The two founders previously studied computer science together at Harvard University.
Corinne Riley, a partner at Greylock, compared Altara’s mission in the physical sciences to the role of site reliability engineers (SREs) in the software industry. Riley noted that just as an SRE inspects an observability stack when a system fails, Altara provides the necessary visibility for hardware-focused R&D.
