RelationalAI

The gap between AI pilots and production-scale impact is where most enterprises get stuck. Moving beyond proof-of-concepts requires more than adopting cutting-edge AI—it demands selecting the right technologies that drive measurable business outcomes. Join Jay (JieBing) Yu, PhD and Alec Coughlin for an insightful conversation on accelerating enterprise AI transformation through practical, experience-driven strategies. Drawing from 20+ years of enterprise AI adoption experience and his perspective as an AI technology leader, Jay will share valuable insights on: ? Bridging the AI pilot-to-production gap—concrete approaches to scale initial investments and realize ROI ? Knowledge Graphs as architectural foundations—how they reduce data complexity and enable unprecedented decision-making velocity ? The power of complex AI—integrating domain knowledge with reasoning capabilities for superior business outcomes ? Last-mile engineering excellence—why implementation details make or break AI initiatives ? Real-world impact stories—including how Blue Yonder reduced code by 20x while building a more scalable AI foundation ??This session will cut through the AI hype cycle to focus on practical solutions that accelerate enterprise AI journeys and deliver tangible business value. #EnterpriseAI #AITransformation #PracticalAI

The gap between AI pilots and production-scale impact is where most enterprises get stuck. Moving beyond proof-of-concepts requires more than adopting cutting-edge AI—it demands selecting the right technologies that drive measurable business outcomes. Join Jay Yu and Alec Coughlin for an insightful conversation on accelerating enterprise AI transformation through practical, experience-driven strategies. Drawing from 20+ years of enterprise AI adoption experience and his perspective as an AI technology leader, Jay will share valuable insights on: ? Bridging the AI pilot-to-production gap—concrete approaches to scale initial investments and realize ROI ? Knowledge Graphs as architectural foundations—how they reduce data complexity and enable unprecedented decision-making velocity ? The power of complex AI—integrating domain knowledge with reasoning capabilities for superior business outcomes ? Last-mile engineering excellence—why implementation details make or break AI initiatives ? Real-world impact stories—including how Blue Yonder reduced code by 20x while building a more scalable AI foundation ??This session will cut through the AI hype cycle to focus on practical solutions that accelerate enterprise AI journeys and deliver tangible business value. #EnterpriseAI #AITransformation #PracticalAI

What do swimming pools, skateboarding and Bitcoin have in common? ?? Join RelationalAI VP of Research ML Nikolaos Vasiloglou at the AI and Future of Finance Conference at Georgia Tech Scheller College of Business (03.20-03.21) during his day one presentation to find out! Nik provided us with a sneak peek of his upcoming presentation - "Knowledge Graphs in the RAG Era" - during an entertaining and insightful 12-minute fireside chat with Alec Coughlin. In addition to answering the question above, Nik provided an excellent mental model for understanding AI-enabled Knowledge Graphs: ?? "Knowledge graph is the language that both machines and humans understand." ?? "You can take an ontology and knowledge graph and basically read it as you see it. You don't have to be an expert. Anyone can do that." ?? "The problem we're trying to solve is making data accessible to everyone and asking questions in the easiest way possible. The semantic layer and knowledge graphs are the basic ingredient." Recent research from Evident reinforces the relevance of these insights to the Financial Services industry. Analyzing the top 167 AI use cases across 50 of the largest banks, they found industry leaders are rapidly evolving beyond efficiency improvements and cost-cutting to: ?? Generating net new revenue streams ?? Enhancing customer experiences These efforts align directly with Nik's insights during the fireside chat and highlight why we're looking forward to his presentation next week. Will you be there? Let us know in the comments! Link below to more information on the AI and Future of Finance Conference and the Evident research mentioned above.

What’s at stake in AI and Finance? One thing is clear: LLMs alone aren’t enough. That’s why we’re excited to sponsor the AI and Future of Finance conference at Georgia Tech Scheller College of Business on March 20-21. Our VP of Research ML, Nikolaos Vasiloglou, will be taking the stage on Day 1 (March 20) to share insights on: - Knowledge Graph and Graph Analytics Using RelationalAI - The next frontier for Financial Services AI - How enterprises can move beyond LLMs to build intelligent applications (Hint: Have a look at his 12 Days of NeurIPS content – link in the comments!) RelationalAI is built for AI-driven financial innovation. The Big Shift: ?? Moving from experimenting with AI to scaling AI-first business models ?? Why knowledge graphs are the missing link in finance AI adoption ?? How banks can embed AI confidence into their operations Curious how AI-first banks are leading the way? Nik’s takeaways from the conference will be shared soon—follow us to stay ahead.

The "factory of the future" is no longer just a vision – it’s here. Leading manufacturers are already using Digital Twins to optimize operations, predict maintenance, and adapt to market shifts in real time. But scaling AI in manufacturing isn’t just about adopting new technology. It requires a data architecture that eliminates silos and enables true enterprise AI. McKinsey & Company puts it bluntly in “Digital Twins: The Next Frontier of Factory Optimization": ?? "Digital twins are set to evolve from a nice-to-have technology into a must-have tool for manufacturers of all kinds." ? But here’s the challenge: fragmented data models and architecture slow down AI adoption. ? And this prevents companies from making the leap from isolated proofs-of-concept to fully operational AI systems. This is where RelationalAI comes in. ?? As a Snowflake native application, we help manufacturers integrate siloed data and unlock AI-powered knowledge graphs - capturing the complex relationships between physical assets, processes, and business goals while your data remains secure within Snowflake. The result: ?? Faster, more accurate decision-making ?? Optimized factory operations ?? AI-Driven insights at scale Want to learn how leading manufacturers are implementing Digital Twins? Check out the full McKinsey article in the comments. Or connect with us to discuss how RelationalAI can accelerate your enterprise AI journey without complex data migrations or security compromises.

Most technical content gets buried in a PDF. But Nikolaos Vasiloglou was not about to let that happen to this 12 Days of NeurIPS series. So he put NotebookLM to the test – grappling with LLM hallucinations, publisher roadblocks, and ethical tangents – to transform deep AI insights into something more accessible. The result: a workflow that turns complex research into digestible podcasts, bringing cutting-edge AI discussions to a broader audience. This is how real innovation happens: by tackling hard problems with creativity, persistence, help from your friends (s/o Vasileios Loukakos) and of course, a sense of humor. Read about it below:

What if you could replace 205,000 lines of code with just 10,000 AI-powered rules? That’s exactly what Blue Yonder did with RelationalAI in Snowflake. On last week’s Q4 earnings call, Snowflake CEO Sridhar Ramaswamy shared that Blue Yonder “processes over 20 billion AI predictions daily” providing "powerful supply chain intelligence," something that's become even more critical as global trade evolves. But most people don’t realize that relational knowledge graphs inside Snowflake, powered by RelationalAI, are helping Blue Yonder get to the next level in their AI journey. At Snowflake BUILD, RelationalAI’s Founder and CEO Molham Aref and VP of Applied Research Jay (JieBing) Yu, PhD illustrated how leading companies are building intelligent applications, highlighting the Blue Yonder case study. 1?? 205,000 lines of business logic reduced to 10,000 rules & SQL statements. Instead of tangled, hard-to-maintain code, Blue Yonder modernized its infrastructure, creating a new intelligent app platform using RelationalAI. 2?? Everything stays inside Snowflake. With RelationalAI running on Snowpark Container Services, both data and logic stay native—eliminating unnecessary complexity. 3?? Goodbye, soul-crushing complexity. No more convoluted applications that are painful to build and maintain. RelationalAI makes mission-critical applications dramatically simpler and more powerful. Hello, intelligent knowledge graph layer supporting many more AI workloads including graph analytics / insights, math optimization, GNN and GenAI. All on RelationalAI technology inside Snowflake. Want to learn more? Watch the 8 minute fireside chat with Jay Yu below. Want to see it in action? Watch the full 28-minute talk, with Molham and Jay linked in the comments.

On last night’s Q4 earnings call, Snowflake CEO Sridhar Ramaswamy described the impressive results they’ve generated through their focus on delivering “the world’s best end-to-end data platform, powered by AI.” ?? As a Snowflake native application, it was especially exciting for us to hear the compelling description of the AI-enabled results RelationalAI client Blue Yonder is generating: "For example, supply chain leader Blue Yonder leverages Snowflake robust data management capabilities and scale to help companies transform their operations by offering AI powered insight." "The Blue Yonder platform processes over 20 billion AI predictions daily to help retailers, manufacturers and logistics providers better manage inventory, optimize delivery and respond to disruption." "It enables their business and their customers to access powerful supply chain intelligence that are deemed impossible to build on their own." ? Global supply chains are under increased pressure as changes in US tariffs have escalated complexity and unpredictability. ?? Reinforced by Nikolaos Vasiloglou, RelationalAI VP of Research ML during his exceptional 12 Days of NeurIPS talks, having modern AI systems capable of dealing with “large behavioral change” is critical:? "Sometimes your customers or the world has a large behavioral change, like a tariff or fraudsters who discover a new way to attack your system." "So now you have to have new features in order to deal with the new change in the world, or deal with this smarter fraudsters" (12 Days of NeurIPS). ? ?? As a result, legacy data and technology stacks are making it substantially more challenging for leaders to run simulations and what-if scenarios while those who have invested in developing intelligent applications are positioned to manage risk and capitalize on opportunities accordingly. ?? Challenges RelationalAI is purpose-built to solve as described by Max De Marzi, RelationalAI Developer Relations Engineer during a recent LinkedIn Live Session “AI, Tariffs + Supply Chains…Oh My.” "So we're going to offer a new solution, which is we're going to solve our problem by building a relational knowledge graph." "Instead, we're going to take our relational data model in tables, and we're going to create this relational knowledge graph that's going to be made up of entities and relationships that are going to be named so that you have some semblance of how things are connected, and pipe it all together into what most people think of as a digital twin." Links to the content above in the comments below.

Going to #MWC25? Come check out RelationalAI and Snowflake reimagining #digitaltwins via #knowledgegraphs

A fascinating trend that appeared across multiple 12 Days of NeurIPS sessions is the emergence of the Language Model Scientist. This specialized role is increasingly being adopted by enterprises at the leading edge of building and deploying AI. ? "I call it the LLM scientist toolkit…This is the part that made me believe that the Language Model Scientist is coming up as a new profession" (Day 8, 0:18), says Nikolaos Vasiloglou, RelationalAI VP of Research ML. All the dimensions for scaling are working independently and, in some cases, together to bring us closer to not only more useful models but also small enough to fit everywhere "AI will reach new levels of efficiency, approaching natural intelligence and become deployable on pervasive devices driven by core innovations in model architecture, software and hardware" (Day 7, 15:06). Understanding that model performance is dependent on the quality of data selection, the emerging role is creating value by improving resource optimization and driving performance enhancement through techniques such as model merging. "This is a way of dynamically merging language models, and I think that's the reason why it is, is actually very popular" (Day 9, 5:14). These skills are especially critical within businesses executing against their vision of replacing application-centric with data-centric architectures, where Language Model Scientists serve as key enablers of intelligent applications. "Smaller high quality data can offer a higher efficiency...They show with 40% less data, you can actually get the same performance" (Day 8, 3:09). Organizations that develop this specialized capability will gain significant advantages in both efficiency and effectiveness as they navigate the increasingly complex AI landscape. Links to the videos for Day 7, 8 and 9 of the 12 Days of NeurIPS content can be found in the comments below.