Data Context for SQL

Give AI a shared understanding of your SQL data.

For connected SQL databases, review and publish business meaning for tables, fields, relationships, and metrics so analysis starts from definitions you trust.

Set up Data ContextStart free
Data Context Definitions editor with table grain and field aliases, descriptions, roles, and review statusData Context Definitions editor with table grain and field aliases, descriptions, roles, and review status
Swipe to inspect the interface

Definitions people can read

Turn physical fields into business language.

Add a description and grain for each table. For every field, review its Alias, Description, Role, and Review status alongside the physical database name.

  • Keep physical names visible for traceability.
  • Use approved aliases and descriptions in analysis.
  • Mark identifiers, dimensions, measures, and timestamps by role.
Definitions editor for coffee sales with table grain, description, physical fields, Alias, Description, Role, and Review statusDefinitions editor for coffee sales with table grain, description, physical fields, Alias, Description, Role, and Review status
Swipe to inspect the interface

Joins and calculations

Approve how data connects—and how the business calculates.

Relationships tell Formula Bot which tables can be joined. Metrics give recurring calculations a shared name and definition.

Relationship editor connecting the coffee sales State field to the earthquake state field with one-to-one cardinalityRelationship editor connecting the coffee sales State field to the earthquake state field with one-to-one cardinality
Swipe to inspect the interface

Relationships

Define the source, destination, join fields, and cardinality so Formula Bot knows how records connect.

Metric editor defining Stores as a distinct count of the coffee sales Area Code fieldMetric editor defining Stores as a distinct count of the coffee sales Area Code field
Swipe to inspect the interface

Metrics

Publish simple aggregations and derived calculations such as Net Revenue with descriptions and formats.

Documents and SQL feedback

Add the meaning schemas miss. Teach from corrections.

Upload business vocabulary and schema notes, then save SQL feedback from chat to steer future queries.

Uploaded Docs screen for adding schema notes, data dictionaries, and business vocabularyUploaded Docs screen for adding schema notes, data dictionaries, and business vocabulary
Swipe to inspect the interface

Uploaded Docs

Add schema notes, data dictionaries, and vocabulary that inform field drafting and answers.

SQL Feedback screen explaining how reviewed query annotations steer future SQLSQL Feedback screen explaining how reviewed query annotations steer future SQL
Swipe to inspect the interface

SQL Feedback

Save correct or incorrect query annotations from chat so reviewed feedback can steer future SQL.

A controlled meaning layer

Describe connected data without changing the data itself.

Data Controls keep excluded data out of Data Context and AI. Drafts, published revisions, and history make it clear which definitions are active.

Data Context describes connected data; it is not the dataset and it does not replace AI Agents.

How analysis should run

Need to configure the analyst too?

AI Agents define analysis instructions, Business Context, output preferences, and presentation defaults.

Explore AI Agents

Compare your options

Decide with evidence, not a sales pitch.

Open a neutral research prompt in the AI tool you already use. Compare capabilities, limitations, and fit before you choose.

01ChatGPTOpen a balanced comparison prompt in ChatGPT.02ClaudeOpen the same research prompt in Claude.03PerplexityResearch the comparison with linked web sources.

AI answers can be incomplete. Check source links, current pricing, and product documentation before deciding.

A shared SQL vocabulary

Teach AI what your SQL data means.

Connect a supported SQL database, review its definitions, and publish the context you trust.

Set up Data ContextStart free