DataHub has released Context Intelligence, a new layer designed to improve the accuracy of AI agents when working with enterprise data systems. The solution addresses a persistent challenge in artificial intelligence applications: the tendency of language models to generate plausible-sounding but incorrect database joins and queries.
Turning Historical Data Into Knowledge
The Context Intelligence platform works by analyzing an organization’s existing SQL query history to construct a semantic index. This index serves as a reference layer that AI agents can consult when formulating database queries, drawing on patterns established by actual queries that have previously worked within the organization.
According to Shirshanka Das, the approach represents a significant shift in how enterprises can leverage their accumulated data work. “For the first time, enterprises can turn years of analyst query history into a living, retrievable knowledge base where agents stop hallucinating joins because they have access to the joins that have worked before, validated by the people who ran them,” Das said.
Addressing AI Model Limitations
The release of Context Intelligence reflects growing recognition within the data management sector that AI models trained on general knowledge struggle with domain-specific database schemas and organizational data architectures. Rather than requiring AI systems to learn these structures from scratch, DataHub’s approach grounds agent responses in historical evidence from within each organization.
The solution sits within the broader AI and machine learning category while addressing specific needs in data management. By providing agents with contextualized information about successful queries, the platform aims to reduce errors and improve the reliability of AI-assisted data analysis across enterprises.
European Data Management Landscape
The launch of DataHub’s Context Intelligence arrives as European enterprises increasingly invest in AI-powered data tools. The European startup ecosystem has seen growing momentum in data management solutions, particularly among companies seeking to implement AI systems responsibly and reliably. Solutions that reduce AI hallucinations and ground language model outputs in organizational reality address regulatory and operational concerns that resonate strongly across the EU, where data governance and accuracy requirements remain paramount.
As organizations across Europe accelerate their AI adoption, tools that improve agent reliability without requiring extensive retraining or fine-tuning represent an important category of infrastructure software. DataHub’s release demonstrates how companies are solving practical implementation challenges that emerge when deploying AI at scale within existing enterprise systems.