A first-class developer environment
Work in Cursor, Claude Code, or VS Code. Version with Git, review with PRs, and deploy with CI/CD. Apply the same rigor you bring to production code to your data's meaning.
Engineering meaning
Turn data into shared meaning
Your data's meaning is scattered across dashboards, SQL scripts, spreadsheets, and tribal knowledge. Credible helps data teams capture it, enrich it with business context, and deliver it as one governed model.
Meaning as code
A real engineering asset, not a fragile artifact
Credible gives data modelers and engineers a production-grade workflow for building governed models—the Capture and Enrich steps of the Credible platform.
AI-powered modeling tools
Credible's MCP tools analyze schemas, query logs, and existing models to suggest documentation, joins, relationships, and calculations—starting from real usage, not a blank page.
A feedback loop for meaning
Run queries instantly, inspect generated SQL, and explore data as you build. See how model changes affect downstream consumers in real time, then test and refine meaning like production code.
Governed context
Make your model useful everywhere
Three layers of enrichment turn a bare schema into a discoverable, secure, AI-ready foundation.
AI-discoverable
Describe fields with #(doc) and index actual values with #(index), so agents find business concepts—not just matching column names.
dimension:
#(doc) Customer segment based on lifetime value
#(index)
customer_segment is lifetime_value ?
pick "High" when > 10000
pick "Medium" when > 1000
else "Low"Access-controlled
Gate sources with authorization rules, and filter rows using secure givens resolved from each caller's verified identity. Sales sees their deals, engineering sees their metrics, and every customer sees only their own rows.
#(authorize) "'sales' in $GROUPS"
source: deals is app_data extend {
measure: deal_count is count()
}
#(secure)
given:
ALLOWED_TENANTS :: string[]
source: tenant_analytics is app_data extend {
where: tenant_id in $ALLOWED_TENANTS
}Rich business context
Capture institutional knowledge—business rules, edge cases, approved definitions, exclusions, and their rationale—beside the fields it governs. That context becomes versioned, searchable, and available with every answer.
measure:
#(doc) Excludes internal test accounts per Finance policy (2024 Q3).
#(doc) Board-approved definition — do not modify without CFO sign-off.
monthly_active_users is count(distinct user_id)
{ where: last_activity > now - 30 days, is_internal = false }The tribal knowledge that used to live in Slack threads and Google Docs now lives in the model—versioned, searchable, and delivered as context wherever it's needed.
Why Malloy
Purpose-built for analytical meaning
Malloy is an open-source language built to encode shared business meaning on top of operational data.
Declarative and intent-driven
Describe what your data represents instead of hand-constructing joins and queries. The compiler handles SQL generation.
Composable
Bundle calculations, definitions, and metadata with the data they describe. Extend and specialize without duplicating logic.
Correct at any grain
Symmetric aggregates, native nesting, and level-of-detail primitives handle analytical problems that routinely break SQL.
Open and stable
Mature, production-ready, backward-compatible, and portable. Your models stay yours, with no lock-in.
Across the data team
Shared meaning is a team effort
Engineering meaning starts with data modelers—but it doesn't stop there. One system, shared understanding, and meaning that evolves as the business changes.
Modelers & engineers
Own the canonical model: entities, relationships, metrics, access controls, and governance.
Analysts & domain experts
Contribute definitions, metadata, and business context through Credible's web UI—without breaking engineering discipline.
AI agents
Surface gaps and suggest improvements from real query patterns, unanswered questions, and missing context.
One model
Deliver the same understanding to every context
Engineer meaning once and govern it centrally, and every downstream experience inherits the same understanding—delivered as context to agents, definitions to dashboards, and governed APIs to your products.
Ready when you are
Engineer meaning. Deliver understanding.
See how Credible helps you capture fragmented data knowledge, enrich it with business context, and deliver it wherever decisions are made.