Data Context Wizardthe data ecosystem’s context layer
A living semantic and knowledge graph of the data layer, for the agentic era. Everything your agents need to be right about your data, provenance-stamped, governed, and kept alive by the fleet itself.
The context layer built for your data ecosystem.
Not a general-purpose AI memory. The graph is native to the data layer: your warehouses, catalogs, metric definitions and the decisions behind them, connected into one living picture. And no more knowledge decay: traditional catalogs go stale because they rely on human updates, while the Wizard maintains itself. Every agent writes back what it learns during its runs, lineage, decisions and fixes, so the next resolution is smarter.
A live map of your data ecosystem
Every cloud, catalog and metric in one graph
Self-maintaining, auto-enriching context
Ask the graph anything
Brings your existing data stack together on day one.
One governed pane over everything you already run: Snowflake, Databricks, BigQuery, dbt, and even your existing catalogs, business glossary and semantic layers. 50+ connectors, no migration project, and connecting is the last manual step. From then on the fleet enriches the graph as it works, documenting tables and classifying PII as a byproduct of doing the job.
No migration, works from day one
No hallucinations, enterprise-scale trust
Single pane of context across your stack
Even connects to your context infrastructure
Governed to enterprise data-governance standards.
The write path is governed end to end, built for regulated enterprises, not demos. PII detected and scrubbed before storage, per-tenant isolation, tamper-evident audit, and encrypted at rest with GDPR right-to-be-forgotten. And every governed write compounds: each incident resolution is recorded back into the graph, so the system gets more reliable at identifying and fixing similar issues over time.
PII detection and PII scrubbing
Per-tenant isolation
GDPR right-to-forget
Your definitions become the source of truth
Better, faster answers with a context layer, for humans and agents.
Fig. 1: Time to answer various knowledge-work questions is much faster with a context layer (in green), as compared to today without it (in black). Illustrative; times are targets.
Missing data context is expensive, especially in the AI era
Fig. 2: $854,000 a year, at least lost on missing data context. Eleven annual cost lines for an eight-person data team, each drawn to scale with the calculation that produces it, grouped into the three failures they belong to. Rates: $100/hr analyst, $150/hr senior, $300/hr executive, 50 working weeks.
“Enterprise data today is still incredibly disparate and messy - and because of that, data agents struggled to answer basic questions across various data architectures amassing structured and unstructured data.”
“The knowledge layer encodes the analytical skills and methodologies that experts use to produce insights… Encoding them as governed primitives is what democratizes the craft.”
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