Enterprise AI is moving beyond chatbots and pilots into production systems that need to work with real business data, governed safely and traceable at every step. DataGallery, an open-source data-agent platform, is addressing one of the biggest challenges holding back this shift: how to let AI systems access and reason over enterprise data without sacrificing security, compliance, or operational control.

What's Holding Back Enterprise AI Deployment?

Most organizations have data scattered across multiple systems, databases, and documents. When companies try to move AI from experimental pilots into real production work, they run into a fundamental problem: their AI systems don't understand the business context, governance rules, or data relationships that make enterprise information usable. DataGallery solves this by creating what it calls a "governed foundation" for enterprise AI agents, allowing them to retrieve information, reason over data, call tools, and evaluate results while staying within defined permissions and safety controls.

The platform has already proven itself in real-world deployments. At a leading bank, DataGallery provides natural-language data access and agent-assisted analytics to approximately 20,000 data analysts and 200,000 management and marketing users. In June 2026, DataGallery-Text2SQL achieved 77.53% execution accuracy on the BIRD benchmark, which evaluates how well AI systems can translate natural language into database queries across complex, real-world databases. The platform ranked fourth on the public leaderboard and first among open-source solutions.

How Does DataGallery Enable On-Device and Local AI Reasoning?

DataGallery works through three interconnected components that allow AI agents to operate locally within enterprise environments while maintaining governance:

• DataAgent: Translates user intent into controlled execution across tools and data systems, handling multi-step workflows like source selection, metric interpretation, tool execution, result validation, and output generation, all within defined permissions, cost, and safety controls.
• Unified Semantic Engine: Provides a governed map of enterprise data, including business definitions, relationships, and permissions, organizing structured, semi-structured, and unstructured data from heterogeneous sources into a business-aligned semantic layer.
• KnowEdge: Converts long-form documents into structured, citation-ready knowledge assets, processing financial reports, research papers, policy documents, and internal materials into document trees with hierarchy, page anchors, section context, and retrieval-optimized chunks.

This architecture functions as an orchestration layer for enterprise data work. Rather than giving AI agents direct access to complex data warehouses, DataGallery provides a business-aligned map of how the organization understands its data. This is particularly important in large environments where a model cannot inspect every table or column for each query. The practical impact includes less repetitive document review, improved traceability of AI-generated answers, and the reuse of institutional knowledge that would otherwise remain locked in files and expert workflows.

Why Does the Enterprise AI Market Matter Now?

The timing of DataGallery's emergence reflects a broader market shift. The global enterprise artificial intelligence market is estimated at USD 20.93 billion in 2025 and is anticipated to reach around USD 592.51 billion by 2035, expanding at a compound annual growth rate of 39.70% between 2026 and 2035. This explosive growth is driven by rising digitalization across end-use industries and the need for AI systems that can actually work with real business data.

The artificial intelligence infrastructure market is also expanding rapidly, accounting for USD 72.02 billion in 2025 and predicted to increase to approximately USD 518.26 billion by 2035, expanding at a compound annual growth rate of 21.82% from 2026 to 2035. Much of this growth is fueled by the growing need for real-time edge AI computation and high-performance AI infrastructure. The demand for real-time AI inference and the growth of Internet of Things (IoT) devices have led to an increasing focus on edge computing solutions, which allow AI inference to be done locally on devices, improving privacy and security while lowering latency and bandwidth needs.

For business and technology leaders, the goal is clear: make enterprise data usable by AI while maintaining governance, traceability, and operational control. DataGallery's approach demonstrates that this is no longer a theoretical challenge. Organizations can now deploy AI agents that work with real business data, understand domain-specific context, and operate within defined safety guardrails. As enterprises move AI from pilots into production at scale, platforms that bridge the gap between raw data and governed, trustworthy AI reasoning will become essential infrastructure.