Starburst
Enterprise AI Needs Data Context, Not Centralization

The Starburst Enterprise Intelligence Platform enables organizations to query, govern, and analyze data across systems, helping teams create value without waiting through multi-year migration efforts.

“The goal is to help customers deliver on their AI ambitions and solutions by working with data where it already lives, rather than forcing it into a new system,” says Matt Fuller, VP of AI/ML Products.

Fuller emphasizes that distributed data is not a temporary phase for most enterprises. It is the operating reality. This shifts the focus from moving data into one location to making it usable, governed, and actionable across environments.

A Four-Layer Approach to Enterprise AI

The Starburst Enterprise Intelligence Platform is designed to operate across distributed data environments. The platform allows teams to work across data lakes, warehouses, operational systems, cloud platforms, and on-premises environments without requiring large-scale relocation. This helps teams generate insights faster while avoiding the cost, complexity, and delay of large migration programs. Underpinning this approach is a four-layer architecture, with each layer addressing a specific challenge that organizations face as they scale AI initiatives.

The foundation is the analytics engine, which addresses the problem of fragmented data. Enterprises often store information across data lakes, warehouses, operational systems, cloud platforms, multiple clouds, and on-premises environments. The analytics engine provides access across these environments, allowing organizations to begin extracting value without forced migration. Above it sits the context layer, which addresses another critical challenge: AI systems need more than data access to produce accurate, trusted results. The context layer enriches data with business meaning, metadata, governance, lineage, access controls, and definitions so AI systems can better understand what the data represents and how it should be used.

The agentic control plane builds on this foundation by coordinating models, tools, agents, and workflows across the platform. This gives the enterprise a way to move from isolated AI interactions to governed AI-driven processes. It allows organizations to move beyond simple question-and-answer interactions toward AI-driven processes that support decisions, coordinate tasks, and initiate action.

At the top of the stack is AIDA, which provides a natural-language interface for business users. Teams can ask questions using everyday business language, interact directly with governed data, and trigger actions through conversational requests, expanding access to insights beyond technical users.

Together, these four layers enable organizations to operate across hybrid environments while supporting the accuracy, scalability, governance, and flexibility required for enterprise AI.

The Rise of the Context Layer

Making data available is only part of the challenge. For AI systems to generate reliable insights, they must also understand what the data represents, where it came from, who can use it, and how it maps to business meaning. Starburst addresses this through a ‘context layer’ that enriches federated data with metadata, governance, and business meaning. Instead of leaving interpretation to guesswork, data is organized into governed data products that carry lineage, access rules, and business definitions.

As Fuller explains, “The combination of providing the business meaning and the data itself gives the proper context to AI.”

This becomes critical when AI systems are used by business users. People do not ask questions in schemas or column names. They use business language. Without a bridge between technical data and business meaning, even the most advanced models can misinterpret results.

By combining broad data access with consistent metadata and business context, Starburst helps reduce hallucinations, improve accuracy, and gives organizations greater confidence and trust as they move AI applications into production.

Federation as the Foundation for Data Access

With meaning and governance in place, how queries are executed across systems becomes just as important. Starburst addresses this through federated data access. The platform allows users to query data across multiple systems through a unified access layer. Query processing can be pushed closer to the data, reducing unnecessary movement. Because data will continue to exist across multiple systems, organizations need a way to operate across that structure while preserving governance and control.

Organizations can derive value from existing data environment while still maintaining compliance with data residency and governance requirements.

A financial services use case shows why this matters. In an anti-money laundering scenario, data was distributed across regions with strict regulatory boundaries. Rather than moving sensitive data across borders, Starburst enabled compute to run close to where the data resided, allowing AI models to analyze it collectively while maintaining compliance controls. The result was faster detection of suspicious activity and a measurable reduction in regulatory risk.

From Insight to Action: Agentic AI

Starburst’s agentic capabilities are designed to help organizations move beyond static dashboards, manual handoffs, and traditional business intelligence workflows. Through AIDA, users can access governed enterprise intelligence through a conversational interface that brings together data access, business context, analysis, and action.

For example, a user could ask the system to identify potential security threats, locate and analyze relevant datasets, assess potential risks, and notify the appropriate team through tools such as Slack. AIDA can also connect to external MCP servers, allowing agents to use approved tools to perform actions in the systems where teams already work.

As Fuller notes, “This is not simply about generating answers, but enabling systems to take action.”

This foundation enables organizations to work with governed information while reducing reliance on static dashboards and manual reporting processes. Teams can interact with data through natural language, uncover relevant insights, and initiate workflows from the same environment, shortening the time between identifying an issue and responding to it.

Built for Openness, Not Lock-In

Starburst is designed to connect the systems enterprises already use. Built on open technologies such as Trino, Apache Iceberg, and aligned with emerging standards such as the Open Semantic Interchange (OSI), the platform integrates across systems without requiring organizations to rebuild their architecture. Its Model Context Protocol (MCP) server enables seamless interaction between agents, tools, and data systems, allowing organizations to extend their existing ecosystems.

This openness extends to how Starburst fits into existing data stacks. For example, organizations using AWS Glue as their data catalog can query and work with their data without first migrating metadata into a new system. This lowers the barrier to adoption and supports a key priority: immediately delivering more value from existing technology investments.

The Future of Enterprise AI Architecture

As enterprises move AI into production, two requirements are becoming unavoidable: access to all relevant data and the context needed to interpret it correctly.

For Starburst, that future focuses on enabling systems to work together across clouds, on-premises environments, and an increasingly complex ecosystem of tools and agents. This approach has contributed to Starburst’s recognition as AI Data Lakehouse Platform of the Year, reflecting its ability to unify distributed data, context, and AI-driven execution in real-world enterprise environments.

Enterprise AI will succeed when the systems behind the models can deliver trusted context at the speed of business. By enabling organizations to work with data across distributed environments, Starburst helps shift the focus from infrastructure complexity to decision execution. In doing so, it moves AI closer to reliable, everyday enterprise use.