Motivation and Values As the demand for reliable AI-driven analytics grows, one of the main challenges is ensuring that AI systems and users rely on consistent and trusted data.

This project aims to build a universal semantic layer (potentially using Cube.dev) to act as a single source of truth for metrics, business logic, and access control. By centralizing these definitions, we can potentially ensure that BI tools, applications, and AI agents all operate on the same, consistent, and verified data model.

This is particularly critical for Text-To-SQL use cases that leverage LLMs and Generative AI: instead of generating answers based on ambiguous schemas, AI agents are grounded in well-defined, governed data definitions that can be easily leveraged. The result from the LLM is then accurate, significantly reducing the risk of hallucinations.

In short, the in-scope platform becomes the data contract layer between raw data and AI, enabling faster insights, consistent reporting, and trustworthy AI-powered analytics.

Goals

• Build Consistent Data Modeling: Model data once and deliver it anywhere, ensuring every downstream tool uses the same business definitions and calculation logics.
• Advanced Agentic Analytics: Combine the semantic layer with LLMs (such as Anthropic Claude) to increase the accuracy of Generative AI through a turnkey AI API.
• Performance and Governance: Implement centralized security policies and caching to boost query performance and reduce operational cloud costs.
• Seamless Integration: Unify data access across diverse platforms, including spreadsheets, BI tools, and custom-built data applications.

Methods

• Semantic Model Exploration: Review and define data models that enrich raw data with business context and reusable metrics, to ensure LLMs can autonomously write SQL queries and build dashboards.
• AI API Integration: Implement a backend (potentially utilizing Cube’s AI API) to ground AI agents in governed data assets, ensuring outputs are aligned with business needs.
• AWS Infrastructure Deployment: Leverage AWS services to ensure robust, secure, and scalable deployment of the analytics platform.

Requirements and Opportunities We invite motivated participants eager to explore the intersection of data engineering and agentic AI.

This project offers:

• Insightful Learning: Gain expertise in building Universal Semantic Layers, integrating LLMs with structured data in SQL, and managing cloud-based data stacks.
• Skill Development: Enhance your understanding of data modeling, Generating AI, and agentic analytics grounded in governed data.
• Transformative Impact: Contribute to solutions that replace legacy pipelines and enable organizations to gain back hundreds of hours through automated, AI-driven reporting.

Together, we can pave the way for an era of agentic analytics where data is accessible, reliable, and tailored to every user's requirements while ensuring strict data privacy and security.

Apply to this project here