DataHub, a leading context platform company, is introducing a major new release of DataHub Cloud that can ingest, structure, improve, and serve trusted context to analytics agents, increasing their accuracy and reliability in production.
DataHub Cloud v1 serves as a context layer that sits between analytics agents, such as Databricks Genie and Snowflake Intelligence, and enterprise data from data stores, including data warehouses and data lakes, to give agents trusted context to get analytics right and smarter with every query, according to the company.
“We turn years of query history into a living knowledgebase, fuse in real-time operational signals, and compound it with every expert correction from the field,” said Shirshanka Das, co-founder and CTO of DataHub. “Every change is timestamped and versioned; agents don't just know the right answer, they know why it changed. That's auditable context, and it's how agents stop hallucinating and start earning trust.”
Unlike retrieval layers that rely on developer-defined schemas at deployment time, DataHub Cloud is a context platform that gives analytics agents a continuously refreshed, expert-enriched source of trusted context about enterprise data at scale, dramatically improving accuracy and reliability in production, the company said.
Before any agent generates SQL, DataHub supplies it with the context that makes the answer trustworthy: unified metadata ingested automatically from more than 100 sources, semantic meaning extracted continuously from years of analyst query history and expert-validated definitions curated by the people who know the data best, the company said.
New features in DataHub Cloud, designed to address the talk-to-data use case, include:
- Context Ingestion addresses context fragmentation, the issue of operational and semantic meaning of data scattered across multiple systems, tools and documents. It builds a unified context graph from structured catalog content, semantic metric definitions from tools, like dbt and Power BI, and unstructured institutional knowledge from Notion, Confluence and similar sources. All context is chunked, embedded and retrievable in real time via GraphQL, MCP or Ask DataHub.
- Context Intelligence converts enterprise query history into a structured semantic index. When an analytics agent receives a question, it retrieves not just schema but validated query patterns that have answered similar questions before, complete with proven joins, filters, and aggregation logic.
- Context Hub gives domain experts a workspace to review, approve and enrich AI-proposed context, collaborate with colleagues and simulate the impact of context changes on text-to-SQL results before publishing. Every expert interaction feeds back into the system so context quality improves continuously.
- Context Activation lets any agent or workflow access and use DataHub context by adding prebuilt skills and an enhanced Agent Context Kit to DataHub Cloud’s full API and SDK and native user experience surfaces built for data practitioners.
In addition, because DataHub Cloud delivers precise, pre-validated context rather than raw schema, analytics agents require significantly fewer tokens to answer each question, reducing inference costs at scale, the company said.
For more information about this news, visit https://datahub.com.