RelationalAI closes the AI value gap with new agentic decision intelligence capabilities for the Snowflake AI Data Cloud. RelationalAI announced at Snowflake's annual user conference, Snowflake Summit 26, a series of new capabilities for Rel, its agentic decision intelligence system that runs natively in the Snowflake AI Data Cloud. With these new capabilities, joint customers can give decision agents the context, reasoning, and post-training needed to take action across the operations that drive the bottom line, including pricing, supply chain, network operations, and resource allocation. Generative AI has unlocked extraordinary value for software development, but most enterprises still see a gap between what AI can do and what they are getting from it across the rest of the business. Today's release introduces the new Rel App, alongside the prescriptive and predictive reasoners, conversational decision intelligence inside Snowflake CoWork, and RelationalAI "push-button" post-training. Together, they give decision agents what they need to act with confidence: a model of the enterprise for context, reasoners as tools, and post-training to turn a general-purpose model into a business expert. "Just like humans, agents have difficulty knowing how to make good decisions," said Molham Aref, Founder and CEO of RelationalAI. "With these capabilities running natively in the Snowflake AI Data Cloud, customers can close the AI value gap by giving their agents the context, tools, and post-training they need to take the best possible action in the face of uncertainty, at machine speed and at economics that scale across the enterprise." The new Rel App captures a shared, governed representation of how a business works: the concepts, relationships, and rules that define how decisions get made. Domain experts can explore the model, follow connections, ask questions in natural language, and reason through decisions, with every interaction grounded in their own data inside Snowflake. Today's release also includes RelationalAI's growing library of coding agent skills, which work across Snowflake CoCo, Claude Code, OpenAI Codex, and GitHub Copilot. Joint customers already use these skills to extend RelationalAI models directly from their preferred development environments, deepening the context their decision agents draw on. The general availability of the prescriptive reasoner gives Snowflake customers a purpose-built tool for solving constrained optimization problems. The predictive reasoner applies graph neural networks to enterprise data inside Snowflake to forecast outcomes like demand, churn, and asset failure. Paired with the prescriptive reasoner, the predictive reasoner gives decision agents a full path from forecast to recommended action in a single workflow, all without moving data off the platform. The RelationalAI suite of rule, graph, predictive, and prescriptive reasoners supports complex multi-domain reasoning in a single workflow. Decision agents working in Snowflake can now combine LLM-based reasoning with domain-specific reasoners, with measurable gains in accuracy and significant reductions in cost. As a launch partner in the Open Semantic Interchange (OSI) initiative, RelationalAI also enables enterprises with existing ontology deployments, such as Palantir, to port semantic models into Snowflake via OSI and run advanced reasoning on RelationalAI with no rebuild required. "At Snowflake, we're focused on enabling secure, high-performance AI directly where data lives," said Amy Kodl, SVP, Worldwide Alliances and Channels at Snowflake. "RelationalAI's Rel App extends these capabilities by introducing powerful reasoning and semantic modeling within the Snowflake AI Data Cloud, helping customers accelerate the development of intelligent agents and decision intelligence systems." RelationalAI also now powers conversational decision intelligence inside Snowflake CoWork letting business users ask ad-hoc questions in natural language and receive governed, semantically grounded answers from RelationalAI's reasoners directly on private data in the Snowflake AI Data Cloud. David Marshall has been involved in the technology industry for over 30 years, and he's been working with virtualization software since 1999. He became a pioneer in the virtualization and cloud computing field - one of the few people in the industry allowed to work with Alpha stage server virtualization software from industry leaders: VMware (ESX Server), Connectix and Microsoft (Virtual Server).Through the years, he has invented, marketed and helped launch a number of successful software companies and products. David holds a BS degree in Finance, an Information Technology Certification, and a number of vendor certifications. He's also co-authored two published books: "VMware ESX Essentials in the Virtual Data Center" and "Advanced Server Virtualization: VMware and Microsoft Platforms in the Virtual Data Center" and was the technical editor for two popular Virtualization "For Dummies" books. With his remaining spare time, David founded and operates one of the oldest independent modern data center publications, VMblog.com. And co-founded CloudCow.com, a publication dedicated to Cloud Computing. Since 2009, and each year thereafter, David has been honored with the vExpert distinction by VMware by Broadcom for his evangelism.Connect on LinkedIn: https://www.linkedin.com/in/davidmarshall/

Rel by RelationalAI recognized with 2026 SIGMOD Research highlight award. May 5, 2026 Sami Davies Last year, the paper Rel: A Programming Language for Relational Data was presented at SIGMOD/PODS International Conference on Management of Data. The paper details the main technical innovations of Rel implemented as part of RelationalAI's relational knowledge graph management system. This year, the paper was announced as one of the SIGMOD Research Highlights of 2026. SIGMOD Research Highlights are awarded to "a set of research projects that exemplify core database research. In particular, these projects address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact." For the 2026 highlights, 10 papers were selected out of roughly 1500 papers from different databases conferences. Recently, Viktor Leis and Thomas Neumann wrote a technical perspective championing the Rel language, which succinctly highlights how Rel "charts a compelling path toward simpler architectures, more reusable data logic, and more principled relational systems." Leis and Neumann discuss the 50-year-old "two-language" status quo, and argue that perhaps the friction between declarative SQL and imperative host languages is a fundamental design flaw, rather than a necessity. The Rel language was designed and implemented with the goal of abolishing the so-called impedance mismatch. While Rel is a declarative language for relational data, it also has key functionalities that enable it to capture the semantics encoded in general-purpose imperative programming languages. By adopting a semantics-first approach grounded in Datalog and first-order logic, Rel introduces a unified model, where the relations serve as the primary abstraction. Furthermore, the Rel language encourages a philosophy of "growing a language", which prioritizes a small core and user-defined extensibility over the more rigid, committee-driven expansion of traditional standards. Rel provides the core tools - modularity and abstraction - that allow users to build the language outward through libraries, and other reusable components. Since the system is rooted in formal semantics, Rel supports reasoning about programs and is highly optimizable. Overall, RelationalAI, Inc. is proud and thankful that the innovation behind the Rel and its dream of abolishing the impedance mismatch, prioritizing extensibility and abstraction, and relying on a semantics-first foundation, are being recognized by the SIGMOD and broader database community.

RelationalAI has secured $22.5 million in investment from Snowflake Ventures and AT&T Ventures to accelerate development of its GenAI-native decision intelligence system. The funding will enhance integration with Snowflake Intelligence and drive customer adoption. The company's technology enables enterprises to optimise and automate decision-making without data movement, using novel LLM training that focuses on customer data. RelationalAI's proprietary algorithmic breakthroughs achieve over 80 times reduction in model training time and costs by combining multi-step reasoning with relational knowledge graphs. AT&T, a long-standing partner, will further deploy enterprise-grade AI solutions through this investment. The collaboration aims to help AT&T leverage its own data to enhance business efficiency and unlock new opportunities.