RelationalAI and Snowflake join forces to revolutionize enterprise AI decision making

RelationalAI and Snowflake join forces to revolutionize enterprise AI decision making

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Relational AI, an artificial intelligence (AI) startup based in Berkeley, California, today announced the release of a product it calls an AI “coprocessor” built for Snowflake, the popular cloud data warehouse provider. The coprocessor integrates relational knowledge graphs and composite AI capabilities into Snowflake’s data management platform. The startup announced its preview availability on Snowflake Summit 2023an annual user conference.

The new offering underscores Snowflake’s drive to become an end-to-end platform for enterprise AI and RelationalAI’s vision for an integrated approach to building intelligent applications. “We are bringing support for these workloads into Snowflake,” RelationalAI CEO Molham Aref said in an interview with VentureBeat. “In the same way that a knowledge graph makes it easier for a human to know what’s going on in the data, it makes it easier for a language model.”

Aref explained how RelationalAI integrates with data clouds and language models and how it allows customers to create knowledge graphs and semantic layers on top of their data.

The coprocessor allows Snowflake customers to run knowledge graphs, prescriptive analytics, and rules engines within Snowflake. This eliminates the need to move data from Snowflake to separate systems for those features. Customers can now build fraud detection, supply chain optimization, and other AI-powered applications entirely within Snowflake.


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Providing businesses with better data

RelationalAI’s AI coprocessor can run securely in the Data Cloud with Container services for snow parks, a new feature that Snowflake announced at this week’s summit. Snowpark Container Services allows customers to run third-party software and applications within their Snowflake account, increasing the value of their data without compromising its security.

RelationalAI has demonstrated impressive early adoption across all industries, including financial services, retail, and telecommunications. Several leading organizations are using RelationalAI today for business-critical workloads in production.

“The amazing thing about language models is that you can ask them general questions and often they can just answer from their internal references,” Aref told VentureBeat. “Sometimes you might ask questions like, ‘How much money did this telecommunications company lose to a fraud last year?’ A language model has never seen cost or financial (company) data. So he can’t answer this question. But if you can point it to where the (company) data resides, and you ask, and it can translate from that question into SQL queries, it will be able to give you the answer to that question.

“So how do you get language models to talk to databases?” he asked. “Well, one way to do that is to get them to talk directly to the databases, which is fine. It works sometimes. But if you have 180 million columns of information, it’s more likely to confuse the language model. So what a knowledge graph lets you do is actually build a semantic layer on top of all these data assets. The knowledge graph makes it easier for a human to know what is happening in the data. It makes it easier for a language model because the language model is trained on text that humans have written and kind of understands the world the same way we understand it using the same terminology.

The future of data clouds and relational knowledge graphs

Aref also shared his vision for the future of computing with the combination of language models, data clouds and relational knowledge graphs.

“I really think these are the three legs of the stool – they will be at the heart of every platform for building decision intelligence in the enterprise,” he said. “Knowledge graphs are key to making everything work because they provide a simplifying abstraction that allows things to talk to each other. So it’s a very important connection point between language models, humans and databases. So it gives us a common language to talk to each other.

RelationalAI is one of the few startups that is tackling the challenge of building intelligent applications with composite AI workloads. The company was founded in 2017 by Aref, who has a background in AI, databases and enterprise software. The company has raised $122 million in funding from investors including Addition, Madrona Venture Group, Menlo Ventures, Tiger Global and former Snowflake CEO Bob Muglia.

Also a member of the RelationalAI board of directors, Muglia praised the company’s technology and vision in a press release.

“The emergence of language models has completely changed the computing landscape,” Muglia said. “As transformative as language models are, their effectiveness can be further amplified when combined with cloud platforms and relational knowledge graphs. I believe this combination will define the future of computing, unlocking powerful capabilities and giving organizations new superpowers.”

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