Quick already offers two natural language querying modes. Dashboard Q&A is intended for questions about data visualized in published dashboards, drawing on the business context that authors have built into each view. Topic Q&A goes further. Authors enrich the data model with business-friendly field names and synonyms, so users can query a curated set of fields in plain language. Dataset Q&A now completes the picture. Users can explore any dataset directly, going beyond what an author has pre-configured, while all the security, permissions, and governance that enterprises expect from Quick remain fully enforced.

While the industry has raced to ship text-to-SQL demos, the real challenge in enterprise BI has never been generating SQL. The challenge is grounding ambiguous business language against complex schemas, enforcing security at every step, and explaining what the system did and why. The agentic system of Quick is purpose-built for this. The model must resolve lexical ambiguity—does “volume” mean row count, revenue, or units shipped?—and map colloquial business language to the precise column names and calculations in the dataset, without a predefined dictionary. Before any query runs, the system searches across all your structured assets (dashboards, datasets, and topics) using a semantic graph that understands how your assets relate to each other. This lets it find the right source even when your question doesn’t use the exact name of a dataset or column. After the source is identified, the system peeks into the data for context like sample values and distributions and uses author-provided field descriptions and business context to disambiguate before using one of the three capabilities available for generating SQL.

This launch also introduces Dataset Enrichment, a streamlined way for authors to ground the system in business context for a single dataset with no topic configuration required. If the business context already exists outside of Quick (in a data catalog, a modeling tool, or a team wiki), authors can upload it directly as a file against the dataset. Field descriptions, intended relationships across fields, custom instructions about specific columns or the dataset as a whole, all of it can be provided in industry-standard formats (YAML, JSON) or as plain-text instructions. The system applies this context automatically to every query, so an author defines it once and every user benefits at scale.

Trust requires transparency. With this launch, we also introduce Chat Explainability. For any intermediate step involved in answering a natural language query, the system now gives users mechanisms to explore what happened under the hood. When structured data capabilities are invoked, users see step-by-step reasoning behind each answer—the generated SQL, the assumptions the agent made, filters it applied, and a plain-language explanation for non-technical stakeholders. There is no black box.