High-performance graph processing and analytics
To bring new efficiency and utility to graph analytics, mining, and databases.
West Campus, Austin, TX
84% employee growth in 12 months
COO of Otta
With datasets becoming larger across almost every business sector, the analysis and management of large-scale and unstructured data are becoming a laborious process. Various existing tech startups provide automatic analysis and data categorization software, but the enormous datasets found in the data landscape of today are yet to have a dedicated solution.
Katana Graph provides a digital analytics engine that facilitates the end-to-end storage, ingestion, and analysis of very large sets of unstructured data. As a result, the company claims its software can help produce graphs and actionable insights in under half an hour, a process that would normally take days when applied to trillions of data points.
Despite many startups aiming to tackle the automatic analysis of raw datasets, Katana Graph holds a unique concept by applying AI, GPUs, and FPGAs to accelerate the generation of large-scale graphs. The startup also aims to make the migration to their analysis tools frictionless, by facilitating connectivity with Python code and the open query graph language OpenCyper.
This company has top investors
Chris Rossbach Pingali(CTO)
Chris worked at several Silicon Valley Startups before working as an Assistant Professor at the University of Texas, Austin. They co-founded Katana Graph in 2020 with Keshav, serving since as CTO.
Keshav served as Chair of Computing at the University of Texas, while working in the Computer Engineering, Computer Science, and Computational Engineering departments. They co-founded Katana Graphs with Chris, serving since 2020 as CEO.
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