Altara, a San Francisco-based startup, has raised $7 million in seed funding to build an AI layer that unifies data scattered across spreadsheets and legacy systems in physical sciences R&D. The round was led by Greylock, with participation from Neo, BoxGroup, Liquid 2 Ventures, and Jeff Dean. Founded in 2025 by Eva Tuecke and Catherine Yeo, the company aims to accelerate failure diagnosis and product optimization for companies working on batteries, semiconductors, and medical devices.
What it does
Altara’s platform ingests data from disparate sources — sensor logs, temperature and moisture readings, historical failure reports — and uses AI to correlate them. The goal is to reduce what the founders describe as a “scavenger hunt” that currently takes engineers weeks or months down to minutes. When a battery fails during cell testing, for example, engineers must manually cross-check multiple data sources. Altara claims its AI automates that triage.
Greylock partner Corinne Riley compares Altara’s role to that of site reliability engineers (SREs) in software: an SRE looks at the observability stack to find what code change caused an outage. Altara aims to be the hardware equivalent — diagnosing exactly what went wrong when a battery or semiconductor wafer map fails.
Approach
Altara is not trying to replace existing research and manufacturing firms. Instead, it provides an intelligence layer that plugs into their existing data infrastructure. This is a less capital-intensive approach than some competitors, such as Periodic Labs and Radical AI, which are building scientific research platforms from the ground up. Riley views AI for physical science as the “next big frontier” and expects rapid development in the sector.
Team
Eva Tuecke previously conducted particle physics research at Fermilab and worked at SpaceX. Catherine Yeo was an AI engineer at Warp. The two met while studying computer science at Harvard University.
Bottom line
Altara’s $7 million seed round reflects growing investor interest in applying AI to physical sciences. By focusing on data integration rather than replacing legacy systems, the company is betting that existing R&D workflows can be dramatically accelerated without requiring a full infrastructure overhaul.