RelationalAI named Snowflake 2026 Telecom Snowflake Product Partner of the Year. June 2, 2026 Company recognized for powering decision intelligence natively in the Snowflake AI Data Cloud, enabling telecom leaders to turn data into real-time, outcome-driven decisions San Francisco, June 2, 2026 - RelationalAI, a leader in enterprise AI, today announced at Snowflake's annual user conference, Snowflake Summit 2026, that it has been named the 2026 Telecom Snowflake Product Partner of the Year award winner by Snowflake, the AI Data Cloud company. For AT&T, AI has to understand the realities of its network, operations, customers, and other details of its business. RelationalAI, Inc. has focused on building practical, telco-grade AI that creates measurable value at scale, said Mark Austin, Vice President of Data Science at AT&T. RelationalAI has been an important collaborator in that journey, including GSMA Open Telco AI and multiple #1 submissions on key industry benchmarks. Their capabilities combined with Snowflake's governed data platform help move AI from simply answering questions to supporting better operational and commercial decisions across the business. Their recognition as Snowflake Telco Partner of the Year reflects the importance of moving beyond generic AI towards systems that can support real decisions in complex enterprise environments. RelationalAI was recognized for its achievements as part of the Snowflake AI Data Cloud, helping joint customers operationalize decision intelligence as a core control plane for AI-native infrastructure. Bringing enterprise decision intelligence natively to the Snowflake ecosystem, RelationalAI transforms Snowflake data into intelligent business decisions through advanced AI reasoning. This recognition from Snowflake highlights the growing importance of decision intelligence as a new layer in the enterprise stack, said Molham Aref, Founder and CEO of RelationalAI. Many enterprise organizations using AI stop at insights like summarizing, retrieving, or generating. RelationalAI bridges that gap with decision-grade reasoning inside Snowflake, and as organizations seek to unlock more value from their data, combining large-scale platforms with AI-driven reasoning will be critical to delivering smarter, more adaptive decision-making. As connectivity infrastructures evolve into strategic platforms, the ability to automate decisions is becoming essential to directly control outcomes, Amy Kodl, SVP, Worldwide Alliances & Channels at Snowflake. RelationalAI combines domain-specific intelligence with a universal decision layer that is built natively on the Snowflake AI Data Cloud. RelationalAI, Inc. is excited to recognize RelationalAI as the 2026 Telecom Snowflake Product Partner of the Year and continue to collaborate to bring even more advanced decisioning capabilities that are governed, auditable, and cross-domain to the AI Data Cloud. RelationalAI continues to set the standard for enterprise decision intelligence, with industry recognition across benchmarks, analyst reports, and standard bodies. Earlier this year, AT&T and RelationalAI contributed to #1 rankings on the Spider and TeleLogs benchmarks. RelationalAI was also named a launch partner for GSMA Open Telco AI, collaborating with AT&T to deliver 33 best-in-telco LLMs (OTel) as part of the launch. RelationalAI was also the only Snowflake-native vendor named the Gartner Magic Quadrant for Decision Intelligence. RelationalAI, together with Snowflake and other ecosystem partners, is helping lead the Open Semantic Interchange (OSI) Industry Initiative. About RelationalAI RelationalAI brings enterprise decision intelligence natively into the AI Data Cloud. Powered by rich semantic models, advanced reasoners, and context-enhanced LLMs, RelationalAI provides agents that understand your business and drive measurable ROI - all without moving data from where it already is. Its goal: AI that can help run a company, not just chat about it. Learn more at relational.ai.

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.