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RelationalAI launches powerful Knowledge Graph Coprocessor for Snowflake users

RelationalAI, a startup that applies artificial intelligence directly to relational data, today announced the general availability of its Knowledge Graph Coprocessor on the Snowflake Data Cloud. The offering, first previewed last year (which we covered in-depth here), enables Snowflake customers to build knowledge graphs and leverage advanced AI and analytics capabilities without moving data out of their Snowflake environment.

“This is a huge win for the customer,” said Molham Aref, cofounder and CEO of RelationalAI, in an interview with VentureBeat. “Normally, in order to do the kinds of things we do — graph analytics, rule-based reasoning, prescriptive analytics, predictive analytics — you have to pull the data back out from Snowflake and put it into a point solution. If you’re a chief data officer, and you spent the last two or three years moving all your data to Snowflake, the last thing you want to do is move it back out.”

The RelationalAI Knowledge Graph Coprocessor is uniquely architected to apply AI directly to structured enterprise data in relational databases like Snowflake. This contrasts with most machine learning and AI approaches that focus on unstructured data like text, images and audio.

“We’re the only system, the only solution that does AI on relational data as broadly and extensively as we do,” said Aref. “The most valuable data [in the enterprise] is actually often stored in a structured form. And historically, machine learning and AI haven’t worked on that data natively or directly.”

RelationalAI reports strong early traction and “infinite demand” for its offering, especially in financial services, telecommunications, retail and consumer packaged goods industries. Customers like AT&T, Block, Ritchie Bros and Blue Yonder have been using the platform to build knowledge graphs that provide a semantic layer over their complex enterprise data.

The launch on Snowflake is a major milestone for the 160-person startup, which has raised $122 million in funding. Snowflake, which has a market capitalization of over $40 billion, highlighted the RelationalAI partnership in recent earnings calls as an example of its growing data cloud ecosystem. 

As large language models like GPT-4o capture the public imagination, Aref sees knowledge graphs playing a key role in applying generative AI in the enterprise. 

“All of these things need something like a knowledge graph as an interface, to be able to talk to the underlying structured data, and to talk to you and understand and leverage the business logic that goes into applications,” he said.

With its Snowflake integration going GA, RelationalAI is well positioned to help companies harness the power of AI on the valuable structured data they already have in the cloud. As interest in generative AI and knowledge graphs grows, the startup’s technology addresses a critical missing piece in the enterprise AI stack.

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RelationalAI, a startup that applies artificial intelligence directly to relational data, today announced the general availability of its Knowledge Graph Coprocessor on the Snowflake Data Cloud. The offering, first previewed last year (which we covered in-depth here), enables Snowflake customers to build knowledge graphs and leverage advanced AI and analytics capabilities without moving data out of their Snowflake environment.

“This is a huge win for the customer,” said Molham Aref, cofounder and CEO of RelationalAI, in an interview with VentureBeat. “Normally, in order to do the kinds of things we do — graph analytics, rule-based reasoning, prescriptive analytics, predictive analytics — you have to pull the data back out from Snowflake and put it into a point solution. If you’re a chief data officer, and you spent the last two or three years moving all your data to Snowflake, the last thing you want to do is move it back out.”

The RelationalAI Knowledge Graph Coprocessor is uniquely architected to apply AI directly to structured enterprise data in relational databases like Snowflake. This contrasts with most machine learning and AI approaches that focus on unstructured data like text, images and audio.

RelationalAI’s architecture enables advanced AI on enterprise data directly within a customer’s Snowflake account. (Image Credit: RelationalAI)

Tapping the potential of structured enterprise data with machine learning

“We’re the only system, the only solution that does AI on relational data as broadly and extensively as we do,” said Aref. “The most valuable data [in the enterprise] is actually often stored in a structured form. And historically, machine learning and AI haven’t worked on that data natively or directly.”


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RelationalAI reports strong early traction and “infinite demand” for its offering, especially in financial services, telecommunications, retail and consumer packaged goods industries. Customers like AT&T, Block, Ritchie Bros and Blue Yonder have been using the platform to build knowledge graphs that provide a semantic layer over their complex enterprise data.

Enabling generative AI for enterprises with knowledge graphs

The launch on Snowflake is a major milestone for the 160-person startup, which has raised $122 million in funding. Snowflake, which has a market capitalization of over $40 billion, highlighted the RelationalAI partnership in recent earnings calls as an example of its growing data cloud ecosystem. 

As large language models like GPT-4o capture the public imagination, Aref sees knowledge graphs playing a key role in applying generative AI in the enterprise. 

“All of these things need something like a knowledge graph as an interface, to be able to talk to the underlying structured data, and to talk to you and understand and leverage the business logic that goes into applications,” he said.

With its Snowflake integration going GA, RelationalAI is well positioned to help companies harness the power of AI on the valuable structured data they already have in the cloud. As interest in generative AI and knowledge graphs grows, the startup’s technology addresses a critical missing piece in the enterprise AI stack.





Author: Michael Nuñez
Source: Venturebeat
Reviewed By: Editorial Team
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