AtScale

Raw LLMs generate answers, but can you trust them? Here’s the reality: without a semantic layer, GenAI systems drift fast. One team’s “revenue” doesn’t match another’s, agents make inconsistent decisions, and outputs can’t be explained or governed. That’s where semantic layers come in. By embedding governed business meaning into AI workflows, with MCP support, Git-native SML, and containerized flexibility, enterprises finally get GenAI that scales with confidence. Curious how this works in practice (and why GigaOm validated it)? 👇 https://lnkd.in/dVNzPXV7

🤖 GenAI is moving fast, but without semantics, it won’t scale. Enterprises are finding that LLMs alone can’t guarantee accuracy, trust, or compliance. Without a semantic layer, “revenue” might mean three different things to three different AI systems. In his latest blog, AtScale SVP Cort Johnson breaks down why semantic layers are now the foundation for enterprise GenAI success, and why GigaOm named AtScale a Leader + Fast Mover in its 2025 Radar Report. 📖 Read the blog: From Models to Meaning: Why Semantic Layers Are the Foundation of Enterprise GenAI Success 👉

🚨 Semantic Layers Are Back in the Spotlight 🚨 From analyst validation to real-world lessons, one thing is clear: semantic layers are no longer optional; they’re the foundation of enterprise AI. In this edition: 📊 The latest GigaOm Radar validates semantic layers as must-have infrastructure 🤖 Why semantics are the missing link between LLMs and business value 🎥 A share-worthy explainer panel breaking down “what is a semantic layer, really?” 👉 Get the full roundup in our latest newsletter

🏆 The 2025 GigaOm Semantic Layer Radar Report made one thing clear: the semantic layer is no longer experimental; it’s mission-critical for enterprise AI. This year, AtScale was recognized as a Leader + Fast Mover for the third year in a row. But the real story is the themes GigaOm highlighted about where semantic layers are headed: 🤖 GenAI & Agentic Readiness: Supplying the business context LLMs need for explainable, enterprise-ready output ⚡ Performance & Cost Efficiency: Sub-second queries and reduced compute costs with autonomous optimization 🔗 Standards & Interoperability: Working across SQL, MDX, DAX, Python, R, BI platforms, and AI agents 🧩 Composable Modeling: Modular, reusable semantic objects governed at the component level 📈 Market Maturity: From emerging tech to established categories, semantic layers are now a cornerstone of enterprise architecture ✅ Business Impact & Trust: Ensuring metrics are consistent, governed, and explainable across every tool and AI system Over the next month, we’ll be diving into each of these themes with blogs, podcasts, and webinars. Follow along as we unpack why GigaOm sees AtScale setting the pace for the future of analytics and AI. 📥 Download the full report here:

The conversation around agentic AI in analytics often stops at the demo stage. A chatbot generates SQL, and a chart appears; everyone nods in excitement. But what happens when that prototype is pushed into production? ⚠️ Business users get different answers in Tableau vs. Excel ⚠️ LLMs “hallucinate” calculations without governance ⚠️ Trust erodes, and the pilot stalls In our joint webinar last week with Distillery, they showed how building on AtScale’s semantic layer with our MCP server changed the game: 🔹 Deterministic NLQ — governed metrics, reusable across tools and agents 🔹 MCP (Model Context Protocol) — giving LLMs safe access to business logic, not raw data 🔹 Reusable business definitions — consistent across BI platforms and GenAI interfaces The live demo wasn’t just a proof of concept. It was a roadmap for how enterprises can: Move beyond “innovation theater” to scalable, production-ready AI Empower agents to reason with context, not just retrieve data Trust AI outputs because they’re anchored in explainable, governed logic This is what we call Agentic BI. It’s not about replacing humans with agents. It’s about giving every agent, whether in Slack, Google Meet, Tableau, or ChatGPT, the same trusted foundation to reason from. Read more in our demo and webinar recap blog → https://lnkd.in/eDngsZ_2 #SemanticLayer #AgenticAI #MCP #NLQ #EnterpriseAI #BI

🏆 GigaOm’s 2025 Semantic Layer Radar Report made one thing clear: success with GenAI depends on more than models. It depends on whether your data foundation is GenAI-ready. That means consistent definitions across BI tools, governed context for LLMs, and semantic models that scale with agentic AI workloads. To help you evaluate your current state, we have created the GenAI-Ready Semantic Layer Checklist — a practical framework that enterprises can use today. Inside, you’ll find: 🔹 How to evaluate your semantic modeling and definitions across systems 🔹 The integration and performance requirements GenAI workloads demand 🔹 Why governance and guardrails are essential for trusted AI 🔹 The capabilities that make your semantic layer truly LLM-ready 📥 Download the checklist and see how prepared your organization is for the age of Agentic AI: https://lnkd.in/eHkw95Jr

AtScale has been recognized as a Leader + Fast Mover in the GigaOm 2025 Semantic Layer Radar Report for 🥉 consecutive years. GigaOm validated the pillars of our approach: 🔗 Interoperability: Works across SQL, MDX, DAX, Python, R, and leading analytics tools 🤖 GenAI & Agentic Readiness: MCP + SML connect LLMs to governed business context 🧩 Composable Modeling: Modular, reusable, enterprise-grade semantic objects 🏢 Enterprise Flexibility: Scales for both large deployments and smaller teams ⚡ Performance & Efficiency: Autonomous query optimization for speed and cost savings 🏆 Independent Validation: AtScale named a Leader + Fast Mover for the third year running 👉 Swipe through the details, then download the full report. 📥 https://lnkd.in/e6BfeHv3

🚀 We’re honored to be recognized as a Leader and Fast Mover in the 2025 GigaOm Semantic Layer Radar Report! Why GigaOm named AtScale a leader: 🔹 A pure-play semantic layer purpose-built for both BI + AI 🔹 Recognition as a pioneer of modern semantic modeling and open semantics 🔹 Continuous innovation with SML, MCP, and GenAI integrations AtScale stands apart by making the semantic layer open, interoperable, and agent-ready; unifying analytics across BI tools, LLMs, and enterprise data platforms. 📥 Download the full report: https://lnkd.in/e6BfeHv3

👀 Industry analysts are weighing in. The semantic layer market is evolving fast, and the conversation is shifting from “what is it?” to “who’s leading the way?” This week, GigaOm will publish its 2025 Radar Report for Semantic Layers and Metric Stores, and we can’t wait to share the results. AtScale has always championed open standards, interoperability, and composability as the foundation for enterprise-scale AI and BI. Soon, you’ll see how these principles measure up under independent analyst review. Follow AtScale to be among the first to read the report when it drops.

🚀 How To Optimize Your Snowflake Utilization Using GenAI + AtScale's Semantic Layer Snowflake has become the foundation for modern enterprise AI and analytics, giving teams the scale and flexibility to transform their business. But are you maximizing your investment? Join this hands-on workshop to learn how combining GenAI tools with AtScale’s Semantic Layer helps you: ✔ Deploy AtScale as a Snowflake Native App ✔ Explore a pre-built semantic cost model for transparency and governance ✔ Pinpoint cost drivers in your Snowflake environment ✔ Use MCP + Claude to generate an AI-powered optimization plan By the end of the session, you’ll walk away with practical, actionable steps to control costs, gain trusted insights, and build a repeatable optimization framework. 👩💻 Speakers: • Brendan Peterson, Senior Product Marketer, AtScale • Frank Lapolito, Solutions Engineer, AtScale Seats are limited for this small group session. Secure your spot today: https://lnkd.in/dKA8DXkv