Torch.AI raises $30M for AI that unifies disparate enterprise data

Torch.AI, a startup developing “network-centric” AI to deliver big data insights, announced that it raised $30 million. The company plans to put the proceeds toward growth as it acquires new customers, particularly U.S. federal agencies in the national security realm, including those operating in high-risk environments.

Most enterprises have to wrangle countless data buckets, some of which inevitably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends. The opportunity cost of this unused data is substantial, with a Veritas report pegging it at $3.3 trillion by 2020. That’s perhaps why the corporate sector has taken an interest in solutions that ingest, understand, organize, and act on digital content from multiple digital sources.

Leawood, Kansas-based Torch.AI, which was founded in 2017, offers one such solution in a platform that connects disparate apps, systems, cloud services, and databases to enable data reconciliation and processing. Torch.AI provides domain-specific pretrained machine learning models for optical character recognition, natural language processing, sentiment analysis, and more that enrich existing data, even if the data is incomplete, flawed, or unstructured. Companies can bring their own models to automate data engineering tasks and workflows ranging from notifying an analyst of an anomaly and its potential impact to recalculating risk or complexity scores, according to Torch.AI.

Torch.AI’s platform functions as an enterprise communication system that provides AI-enhanced data transformations, ingesting data from any source. It decomposes data into smaller, normalized bits with visibility and transparency into data lineage and source integrity, delivering “tagging on ingest” capabilities to help users organize and correlate information. In addition, Torch.AI provides a suite that maintains a hardened cybersecurity posture and tools that make the implementation of compliance regulations and policies ostensibly easier.

“Most data enablement implementations and analytics suffered from internal data engineering challenges. When we engaged with companies across the U.S., we heard the same thing: doing almost anything meaningful with data was too complicated and took too long,” CEO Brian Weaver told VentureBeat via email. “We discovered we could use the efficiency of an advanced application of machine learning to instantly understand and describe data with atomic detail, in memory and while it is still in motion. We patented the concept and started developing our platform, which intelligently connects all a company’s applications and business systems by overlaying a ‘synaptic mesh.’”

The global big data and business analytics market was valued at $169 billion in 2018 and is expected to grow to $274 billion in 2022, according to Statista. While Torch.AI’s AI-powered data reconciliation is more holistic than most, it has a number of competitors, including BackboneAI, which last year emerged from stealth with a product designed to unify enterprise data sets with AI. There’s also Tamr, a Cambridge, Massachusetts-based startup that uses machine learning to speed up data analytics workflows. And there’s Quantexa, which last July raised $64.7 million to further develop its AI platform that extracts insights from big data.

But Torch.AI claims to have quickly established itself in the private sector with a roster of blue-chip private clients including Microsoft, H&R Block, and General Electric. The company also says it’s “moving strongly” into the government sector, where its technology has been deployed across over more than a dozen U.S. federal agencies.

“Today, our customer base spans Fortune 100 companies to mission critical elements of the U.S. government. Typically, our clients come to us after suffering poor analytic outcomes or failing decision systems,” Weaver said. “One of the main benefits and differentiator of our platform is that our clients don’t need to change their internal IT infrastructure; rather, the platform overlays existing data and systems.”

WestCap Group led the series A funding, which is 50-employee Torch.AI’s first public funding round.

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