Katana Graph Secures $28.5 Million Series A Financing Round led by Intel Capital

By: Katana Graph

February 24, 2021

Katana Graph Secures $28.5 Million Series A Financing Round led by Intel Capital

Graph processing, AI, and analytics company will use the new investment to accelerate product roadmap execution, sales, and marketing activities

Katana Graph, the high-performance scale-out graph processing, AI and analytics company, announced today that it has completed a $28.5 million Series A financing round led by Intel Capital with participation from existing and new investors including WRVI Capital, Nepenthe Capital, Dell Technologies Capital, and Redline Capital. Intel Capital investment director, Vijay Reddy, will join Katana Graph’s board of directors, while Dell Technologies Capital president, Scott Darling, will join as board observers.

“We are delighted to welcome our new investors,” said Keshav Pingali, CEO and cofounder of Katana Graph. “These investments will let us accelerate our product roadmap execution, and continue to expand the use of our technology across a range of industries and innovative applications.”

Katana Graph’s platform extracts actionable insights from massive unstructured data sets, using high-performance graph algorithms. At the heart of Katana Graph’s solution is the Katana Graph Engine with its accompanying partitioner, communication, virtualization and storage technology modules, which are the culmination of more than a decade of advanced research in graph technology and high performance computing.

“Katana Graph’s platform helps large enterprises make sense of their large unstructured data sets, and we’re seeing this demand across a variety of industries from social networks to biomedical and pharmaceutical research,” said Anthony Lin, managing partner and Head of Intel Capital. “We are excited to lead Katana Graph’s new funding round based on our collaboration last year where Katana Graph’s technology was optimized for Intel®️ Xeon® processors and Xeon-based clusters. We’re looking forward to helping Katana Graph accelerate their growth.”

“We are delighted to be investing in Katana Graph and its elite team,” said Scott Darling, president of Dell Technologies Capital. “The Katana platform is a breakthrough solution that integrates data ingestion, querying, and analytics with unprecedented scale and performance to address the data deluge problem for unstructured graph data. We are also excited to back the company given Dell Technologies’ and Katana Graph’s shared roots in the University of Texas at Austin.”

“I am excited and honored to provide initial capital and partner with professor Keshav Pingali and professor Chris Rossbach to provide the most comprehensive platform for large-scale graph data mining, query and analytics,” said Lip-Bu Tan, chairman of Walden International and founding managing partner of WRVI Capital. “I am pleased to have Intel Capital, Dell Technologies Capital, Nepenthe Capital and Redline Capital join us in building Katana Graph into the leading graph computing platform for multiple vertical markets.”

The Series A financing builds on an exceptional year for Katana Graph, which saw a rapidly-growing number of enterprise clients in the pharmaceutical, fintech, identity, security, and EDA market segments, as well as strong momentum in the explosive big data analytics market. Katana’s leadership team also includes industry veterans with track records of running successful technology businesses or divisions, including Chris Rossbach, CTO and co-founder, who has held senior roles at VMware and Microsoft Research, and Farshid Sabet, Chief Business Officer, who was GM of Edge AI at Intel. N. R. Narayana Murthy, founder of Infosys and Amy Chang, board member at Procter & Gamble and Cisco, are board advisors for Katana.

Key features and benefits of the Katana Graph include:

  • Ability to handle massive unstructured data and integrate complex pattern mining workloads on large enterprise data sets and knowledge graphs
  • Superior performance for complex graph AI, graph pattern mining and graph analytics algorithms
  • Support for heterogeneous clusters of computing resources including x86 CPUs, ARM CPUs, GPUs and other accelerators
  • Ease of development of high-performance graph AI, graph pattern mining and graph analytics applications, and seamless integration with graph querying