Announcing Cube D3

Today we are launching D3 ("data in cube"), an agentic analytics platform powered by the Cube semantic layer.

Launching D3 feels like coming full circle. We built Cube more than 5 years ago to power a side project—a Slack AI chatbot for analytics. While AI capabilities were very limited back then, we discovered enormous value in extracting Cube from that project and making it a standalone universal semantic layer. We published it as an open-source project, and it took off. Today, thousands of organizations use both our open-source and cloud products to power business intelligence and embedded analytics workloads.

In many ways, Cube was ahead of its time. When ChatGPT came out, we felt that this was the moment we had built Cube for and had been waiting for all along.

What is D3?

AI has made tremendous progress since then, and we need a semantic layer more than ever before. It provides unique context about data to AI agents while establishing a foundation for correct and trusted AI results. In the last two years, the Cube community has built numerous AI-first experiences by combining state-of-the-art LLMs with the open-source Cube Core semantic layer—creating internal analytics agents, external-facing chat interfaces, and operational workflows. Now with D3, we're introducing a first-party agentic analytics platform built entirely with the Cube semantic layer. It can be used to both surface insights internally and embed agentic analytics functionality into external apps.

D3 is unique because it was built from first principles for AI-augmented workflow and is fully based on semantic understanding of data—from the shared semantic model to workbooks and reports layers. We believe in the synergy between AI agents and humans in an augmented workflow. AI-generated queries or reports can be instantly modified by humans and vice versa. AI can handle routine tasks like updating workbook filters, maintaining data models to match CDW tables, and reorganizing data apps layouts, while humans focus on deriving insights from the data.

With D3, we are introducing several core experiences:

Analytics Chat. A user-friendly chat interface that allows non-technical users to quickly access curated data from semantic models and workbooks, complete with summaries and insights.
Workbooks. A reimagined analytics workbook interface that combines traditional features—point-and-click functionality, table calculations, SQL, and Python—with AI-driven capabilities.
Data Apps. Similar to Replit, Lovable, and v0, but specifically for data applications. Create and deploy data apps using modern frameworks, powered by your semantic layer and workbooks. Share them internally or embed them in existing applications.
Semantic Modeling. AI-powered semantic model development that can create Cube data models from scratch, automatically sync with upstream data mart changes, and implement advanced analytics including cohort analysis, funnels, and more.

Semantic Layer and Semantic SQL

All these experiences are built around the Cube semantic layer and Semantic SQL.

For AI to be truly useful in analytics workflows, it needs a semantic layer to understand business context. But this alone isn't enough—AI also needs a structured, governed way to query and extend this layer. We can't just feed AI metric definitions and hope it generates correct queries. Effective governance requires AI to query the semantic layer rather than the warehouse directly. This ensures semantic layer definitions are used correctly. Put simply, AI needs a query language for the semantic layer.

What makes an ideal query language? It should be well-established, thoroughly documented, and widely discussed on platforms like Stack Overflow. This ensures out-of-the-box AI models already "know" it well. The language must also let users directly query semantic objects while flexibly creating ad-hoc dimensions and measures.

SQL stands out as the obvious choice.

D3 agents use Semantic SQL to query semantic definitions directly and create ad-hoc calculations on the fly. This strikes an ideal balance between governance and flexibility. Users can review and modify these Semantic SQL queries through a visual editor or by editing the SQL code directly.

Ad-hoc calculations can be saved and reused across workbooks and, when needed, added to the shared semantic model.

This balanced approach to flexibility and governance creates the foundation for AI-enabled self-serve analytics. With AI assistance, data users can quickly access governed data in the semantic layer while extending their analysis through custom definitions and metrics as needed.

For Humans and Agents

D3 is designed to be used by both humans and AI agents. As organizations increasingly delegate tasks to AI agents, these agents will need access to data insights. We envision a multi-agent ecosystem where sales, marketing, logistics, and other AI agents can seamlessly request insights from D3 agents.

D3 agents are already accessible through MCP (available now in Claude Desktop) and A2A protocols. We're committed to supporting these and new inter-agent communication protocols.

Multi-Agent Architecture

To summarize, D3's approach divides analytics building into three distinct problems:

Building the Semantic Layer
Building Semantic SQL on top of the Semantic Layer
Building Visualizations using Semantic SQL

Each of these areas incorporates multiple AI safeguards—including compilation checks, linters, best-practice prompts, documentation, and memory retrieval. While other solutions try to tackle all three problems at once, our approach makes the process more reliable for AI by treating them as separate, independent components.