Follow Datanami:
March 17, 2021

Torch.AI Looks to Replace ‘Store and Reduce’ with Synaptic Mesh

(Michael Traitov/Shutterstock)

Torch.AI, the profitable startup applying machine learning to analyze data “in-flight” via its proprietary synaptic mesh technology, announced its first funding round along with expansion plans.

The Series A round garnered $30 million, and was led by San Francisco-based WestCap Group. As its customer base expands, Torch.AI said Wednesday (March 17) it would use the funds to scale its Nexus AI platform for a customer base that includes financial services, manufacturing and U.S. government customers.

The three-year-old AI startup’s software seeks to unify different data types via its synaptic mesh framework that reduces data storage while analyzing data on the fly.

“There’s just too much information, too many classes of information,” said Torch.AI CEO Brian Weaver. Hence, enterprises coping with regulatory and other data governance issues are finding they can’t trust all the data they store.

Working early on with companies like GE (NYSE: GE) and (Microsoft NASDAQ: MSFT) on advanced data analytics, Weaver asserted in an interview that current technology frameworks compound that complexity. The shift to AI came while working with a financial services company struggling to process huge volumes of real-time transactions.

“We figured out that we could use artificial intelligence just to understand the data payload, or the data object, differently,” Weaver said.

The result was its Nexus platform that creates an AI mesh across a user’s data and systems, unifying data by “increasing the surface area” for analytics. That approach differs fundamentally from the “store and reduce” approach in which information is dumped into a large repository, then applying machine learning to make sense of it to cull usable data.

“I’ve got to store it somewhere first, then I’ve got to reduce [data] to make use of it,” the CEO continued. That approach “actually compounds [data] complexity…impedes a successful outcome in a lot of ways and introduces at the same time a lot of risk.”

Torch.AI’s proprietary synaptic mesh approach is touted as eliminating the need to store all those data, enabling customers to analyze the growing number of data types “in flight.”

“We decompose a data object into the atomic components of the data,” Weaver explained. “We create a very, very rich description of the data object itself that has logic built into it.” The synaptic mesh is then applied to process and analyze data.

Hence, for example, a video file could be used to analyze data in-memory, picking out shapes, words and other data components as it streams.

The AI application builds in human cognition to make sense of a scene. “My brain doesn’t need to store it, the scene, to determine what’s in it, Weaver noted.

“That’s sort of our North Star: Making sense of messy data” by applying AI to unify the growing number of data types while reducing the resulting complexity.

“If you think about these workloads, people are actually working for the technology, having to stitch all this stuff together and hope it works. Shouldn’t the technology truly be serving the [customer] who has the problem?”

Recent items:

The Past and Present of In-Memory Computing

The Data and AI Habits of Future-Ready Companies

–Editor’s note: A longer version of this story was originally posted to sister website