Your Data Estate.
In Context.
Genesis connects your systems, their dependencies, and the business knowledge behind them—giving people and AI agents a shared understanding of the environment they work in.
Knowing the Code
Is Only the Start
Data work depends on how your systems fit together and what the business expects them to do. Context Graph connects the technical assets to their dependencies and business meaning, giving people and agents a shared understanding.
The Systems and Code
Databases, repositories, pipelines, notebooks, dashboards, and related assets.
How They Fit Together
Data movement, execution dependencies, and available runtime context.
What the Business Knows
Team Wiki explanations, calculation conventions, caveats, and lessons linked to the assets they concern.
Build a Living
Data Estate
Start with the sources you can access. Genesis builds useful context, connects new evidence as it arrives, and revisits knowledge when systems change. Select a stage to explore.
Start with the Sources You Can Access
Scanners inspect metadata, database definitions, APIs, SQL, Python, and configuration files. Available Snowflake and Databricks audit signals can also reveal external tools and the assets they touch. Direct scans add more authoritative detail as connections become available.
Connect the Assets. Add the Meaning.
Genesis links assets through data movement and execution dependencies, then anchors Team Wiki explanations to the relevant entities. References to systems not yet scanned can remain as placeholders. Later discoveries reconcile matching assets; ambiguous references stay unresolved.
Keep Context Useful
as Systems Change
Team Wiki retains the explanations, conventions, and lessons linked to your data assets.
Manual and scheduled scans refresh the graph. When relevant assets change or disappear, Genesis identifies the attached wiki records that need re-evaluation, updating affected knowledge while preserving what remains valid.
How Revenue Is Calculated
Revenue reporting excludes cancelled orders and uses the transaction date for the reporting period.
@daily_revenue · @orders · @revenue_model.sql
Known caveat
Replayed order events must not be counted as new sales.
Example content to show how knowledge connects to assets.
From Connected Context
to Better Decisions
Agents can search by meaning, follow dependencies, and retrieve the knowledge attached to an asset. Here’s what that makes possible.
Trace the metric. Retrieve the rules.
Follow the revenue dashboard back through its pipeline, code, and source data. Retrieve the attached Team Wiki record for calculation conventions and known caveats.
Example: see whether cancelled orders are excluded and how replayed events should be handled.
See What Relies on the Asset
Explore downstream pipelines, models, and reports before changing a table. Use upstream relationships and source details to investigate where a data issue may have started.
Use the connected evidence to assess impact and plan the next steps.
Bring the Dependencies With the Work
Identify related assets, code, and business knowledge to inform migration scope. Context Graph supplies the understanding; Prover verifies migration results against the source system.
Context Graph and Prover play complementary roles: understand the environment, then verify the result.
The Details
Behind the Map
What does Genesis discover?
Scanners inspect metadata, database definitions, APIs, SQL, Python, and configuration files. Available Snowflake and Databricks audit signals can reveal external tools and the assets they touch—even before each tool has a dedicated connection. Direct scans add more authoritative detail.
How does incomplete information get connected?
Scanners contribute what they know. References to unscanned systems can remain as placeholders; later discoveries resolve references and unify matching assets. Ambiguous references stay unresolved rather than being forced into a match.
How does the knowledge stay current?
Team Wiki records explain the purpose, business context, and operation of assets, with explicit links to the graph. Relevant changes found during manual or scheduled scans invalidate affected records’ grounding for re-evaluation. Updates use the changed context and existing explanation to preserve what remains valid.
Can we include proprietary systems?
Custom scanners can bring proprietary systems, APIs, metadata tables, exports, and files into the same graph. Teams can extend asset types, properties, and relationships beyond standard table-to-table lineage. Coverage reflects configured sources, permissions, scan results, and enabled features.