RelationalAI, the pioneer of the relational knowledge graph coprocessor for data clouds, is announcing new capabilities for the Snowflake AI Data Cloud, expanding the potential of AI-powered intelligent apps. With this collaboration, RelationalAI empowers enterprises to create more powerful AI apps with a variety of new capabilities, driving more intelligent, data-centric apps without necessitating data movement or incurring additional architectural complexities.
In this release, RelationalAI delivers a slew of new features that enable organizations to close the gap between knowledge and app semantics and their data. The result is more intelligent applications armed with the ability to execute prescriptive, predictive, graph, and rules-based reasoning.
“These new capabilities open up new possibilities for what customers can do with intelligent apps in Snowflake—moving from reactive analytics to reasoning-powered decisions,” said Molham Aref, CEO of RelationalAI. “We’re proud to offer the most complete foundation for building semantics-aware, AI-native applications on top of enterprise data.”
There are several new, expansive features within RelationalAI’s release, including support for next-gen large language model (LLM) question-answering via text-to-reasoner. Introducing text-to-SQL paradigms, RelationalAI extends question-answering based on retrieval-augmented generation (RAG), driving more trustworthy responses to queries essential for decision making.
Additionally, with integrated prescriptive reasoning, applications can utilize mathematical optimization solvers to make more optimal decisions based on precisely defined constraints and business objectives. This allows apps to be able to reason over more complex domains, such as supply chain planning. Expanded support for graph reasoning, including algorithms such as path finding and egonet analysis, further improves apps’ abilities to comprehend and navigate intricate relationships for data harbored in complex domains.
With integrated prescriptive reasoning for graph neural networks (GNN), applications learn from both the structure and the semantics of data to predict outcomes, unlocking value for use cases such as demand forecasting, churn prediction, and risk scoring. Finally, support for Snowflake Semantic Views enables enterprises to utilize business semantics from the RelationalAI knowledge graph to increase the accuracy of Cortex Analyst while delivering rich dimensional models for BI.
“Partnering with RelationalAI is critical as our customers evolve from simply managing data to making informed decisions where their data lives, in Snowflake’s AI Data Cloud,” said Unmesh Jagtap, director of product management, applications, Snowflake. “RelationalAI’s knowledge graph has the potential to be a game changer for customers looking to harness AI within their existing Snowflake environments, making the process simple and streamlined. These new capabilities make the offering even more powerful, helping Snowflake customers realize the full potential of their data.”
To learn more about RelationalAI’s latest Snowflake capabilities, please visit https://relational.ai/.