"Imagine if you're a company building next-generation batteries, and a battery fails during the cell testing in the R&D process. A team of engineers has to go in and manually check a lot of different sources of data, anything from their sensor logs to their temperature data, moisture data. They cross-check 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. Altara claims that its AI dramatically slashes the time required for this process, condensing weeks of manual data triaging into minutes."
"Corinne Riley, a partner at Greylock, compares what Altara is doing in the physical sciences to the role of site reliability engineers in the software world. If a system fails, an SRE will go in, and they'll go look at the observability stack of the company."
Altara, a startup based in San Francisco, has developed an AI platform to address data fragmentation in industries like batteries and medical devices. The platform aims to streamline the process of diagnosing failures by consolidating data from various sources, which traditionally requires extensive manual effort. Founded by Eva Tuecke and Catherine Yeo, Altara has secured $7 million in seed funding. The AI technology significantly reduces the time engineers spend on data analysis, transforming weeks of work into mere minutes, thereby enhancing product development and failure resolution.
Read at TechCrunch
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