DataGallery Builds Data Foundation for Enterprise AI Agents

In July 2026, DataGallery helped business and tech leaders unlock the value of enterprise data for AI, while preserving governance, traceability, and operational control. DataGallery is an open-source data-agent platform for enterprises that need AI systems to work with real business data. As organizations all over the world increasingly move AI from pilots into production, data assets, business semantics, domain knowledge, and execution tools often remain scattered across systems, with inconsistent quality and limited context. DataGallery brings these elements together in a governed foundation, enabling agents to retrieve information, reason over data, call tools, and evaluate results within enterprise data environments. For business and technology leaders, the goal is quite clear and straightforward, which is to make enterprise data usable by AI while maintaining governance, traceability, and operational control.

DataGallery’s enterprise focus is reflected in both benchmark performance and production deployments. In June, DataGallery-Text2SQL achieved 77.53% execution accuracy on the BIRD benchmark, which evaluates Text-to-SQL performance across complex, real-world databases. It ranked fourth on the public leaderboard and first among open-source solutions. 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. The deployment illustrates how governed enterprise data, business semantics, and domain knowledge can be turned into practical AI-assisted workflows.

Its architecture revolves on three capabilities:

• DataAgent translates user intent into controlled execution across tools and data systems.
• The Unified Semantic Engine provides a governed map of enterprise data, including business definitions, relationships, and permissions. 
• KnowEdge turns long-form documents and expert knowledge into structured, retrievable assets for AI systems.
• Together, these components provide a foundation for enterprise AI applications across analytics, knowledge work, research, and industry-specific workflows.

This is like an orchestration layer for enterprise data work as it translates user intent into a controlled sequence of tasks, including source selection, metric interpretation, tool execution, result validation, and output generation. This allows agents to handle multi-step workflows rather than isolated questions. DataAgent supports data analysis, feature engineering, and knowledge-based construction while operating within defined permissions, cost, and safety controls. DataAgent therefore functions as an execution layer for repeatable data work, with DataGallery providing the context, guardrails, and evaluation required for production use.

Impact on the AI Market

The Unified Semantic Engine connects structured, semi-structured, and unstructured data from heterogeneous sources and organizes it into a governed semantic layer. This includes tables, columns, relationships, metric definitions, entities, business terms, and permissions. Instead of giving agents direct access to a complex data warehouse, it 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.

KnowEdge converts long-form documents into structured, citation-ready knowledge assets. It processes financial reports, research papers, policy documents, and internal materials into document trees containing hierarchy, page anchors, section context, summaries, and retrieval-optimized chunks. Agents can search across complex documents, assemble evidence, cite sources, and reason over domain-specific material with reduced manual review. 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.

Impact on the Enterprise Artificial Intelligence (AI) Market

The global enterprise artificial intelligence (AI) market size is estimated at USD 20.93 billion in 2025 and is anticipated to reach around USD 592.51 billion by 2035, expanding at a CAGR of 39.70% between 2026 and 2035.

According to Precedence Research, the market serves as a crucial organizational resource for business performance at all levels of the organization. Some businesses use artificial intelligence (AI) technology to analyze their consumers, spot fraud and other hazards, and use machine learning to take preventive action.

One of the main reasons fueling the market expansion is the end-use industries' rising digitalization. Additionally, the manufacturing industry has experienced tremendous development as a result of new technologies including edge computing, augmented and virtual reality (AR/VR), industrial robots, self-driving cars, digital manufacturing, industrial internet of things (IIOT), and digital manufacturing.

Impact on the Artificial Intelligence (AI) Infrastructure Market

The global artificial intelligence (AI) infrastructure market size accounted for USD 72.02 billion in 2025 and is predicted to increase from USD 91.21 billion in 2026 to approximately USD 518.26 billion by 2035, expanding at a CAGR of 21.82% from 2026 to 2035.

According to Precedence Research, the market is driven by the growing need for real-time edge AI computation and high-performance AI infrastructure. The market has been expanding steadily due to the rising need for AI-driven solutions in industries including healthcare, banking, retail, manufacturing, and automotive. Because of their capacity for parallel computing, graphics processing units (GPUs) are frequently employed to accelerate artificial intelligence workloads.

The demand for real-time AI inference and the growth of IoT devices have led to an increasing focus on edge computing solutions. By allowing AI inference to be done locally on devices, edge AI solutions improve privacy and security while lowering latency and bandwidth needs. The artificial intelligence (AI) infrastructure market is expanding quickly, but it still faces several obstacles, such as interoperability problems, ethical dilemmas, skill shortages, and privacy difficulties with data. Resolving these issues will be essential to maintaining the market's long-term growth.