Agents that build and maintain your data platform
Agents accelerate the work of building and maintaining your data foundation, grounded in your enterprise context and approved by your team.
The vault builds itself. The meaning doesn't.
The vault is automated. Source analysis, business alignment, and conceptual modeling stay manual, and that is where most of the time and cost actually sits.
Source analysis stays specialist-bound
New sources still wait on a handful of specialists.
Source analysis stays specialist-bound
New sources still wait on a handful of specialists.
Semantic decisions live in tribal knowledge
Business meaning sits in people's heads, where agents cannot read it.
Semantic decisions live in tribal knowledge
Business meaning sits in people's heads, where agents cannot read it.
Data products lag the demand
Defining measures, contracts, and semantics falls behind every quarter.
Data products lag the demand
Defining measures, contracts, and semantics falls behind every quarter.
AI on raw schemas guesses with confidence
Without governed context, agents return confident wrong answers.
AI on raw schemas guesses with confidence
Without governed context, agents return confident wrong answers.
Introducing Agents
An enterprise context store paired with a network of AI agents, built on the structured metadata your data foundation already produces. Agents reason from accumulated context across deployments, while every proposal stays in front of a human before it reaches production.
From metadata to governed data products
Accumulate enterprise context
Read your existing vault metadata, or build the graph from scratch during onboarding.
Agents propose, humans validate
Versioned business definitions and contracts
Generated semantic views, native to your platform
Metadata-only, LLM-agnostic execution
Visual transformation design
Accumulate enterprise context
Transformations are defined using a graphical mapping builder that makes logic explicit, reviewable, and understandable.
Read your existing vault metadata, or build the graph from scratch during onboarding.
Abstract mappings for reuse
Agents propose, humans validate
When logic becomes repeatable, it is promoted to an abstract mapping and reused across multiple datasets, eliminating copy-paste patterns.
Agents never guess: they propose mappings, models, and data products grounded in the context you provided.
Governed metadata and versioning
Versioned business definitions and contracts
Every mapping, pipeline, and release is versioned, traceable, and governed as part of the VaultSpeed metadata foundation.
Every approved definition becomes a versioned contract with lineage back to source.
Deterministic code generation
Generated semantic views, native to your platform
Execution code is generated consistently from governed metadata and templates — not manually rewritten.
Views populate Snowflake Cortex Analyst, Databricks Genie, and Microsoft Fabric directly.
Open execution model
Metadata-only, LLM-agnostic execution
Generated code runs in your chosen execution environment (dbt, Snowflake, Databricks, BigQuery, Synapse) — without a VaultSpeed runtime.
Bring your own model provider: Azure OpenAI, Anthropic, a privately hosted endpoint, or your data platform's native AI service. Agents work on metadata only and never touch your production data, so context stays inside your environment.
The context that makes AI safe in production
The Enterprise Context Store
Every business definition, mapping, and relationship across your estate, captured as a structured graph the agents reason from.
The Enterprise Context Store
Every business definition, mapping, and relationship across your estate, captured as a structured graph the agents reason from.
The Enterprise Context Store
Every business definition, mapping, and relationship across your estate, captured as a structured graph the agents reason from.
Skill-first by design
Data management veterans encoded the playbook into skills your agents use out of the box.
Skill-first by design
Data management veterans encoded the playbook into skills your agents use out of the box.
Run on smaller, cheaper models
The heavy lifting lives in the graph and skills, so agents reach the right answer with smaller AI models and fewer tokens.
Run on smaller, cheaper models
The heavy lifting lives in the graph and skills, so agents reach the right answer with smaller AI models and fewer tokens.
Lineage end to end
Every transformation, mapping, and definition is traceable from source column to consumer, derived from the model and ready for any audit.
Lineage end to end
Every transformation, mapping, and definition is traceable from source column to consumer, derived from the model and ready for any audit.
Generated semantic views
Views are generated from data product definitions and stay in sync as the model evolves, with no drift and no manual updates.
Generated semantic views
Views are generated from data product definitions and stay in sync as the model evolves, with no drift and no manual updates.
Bridges to your platform and your catalog
Connectors push governed metadata into your data platform and your business catalog, so the same model is visible to engineers, stewards, and AI tools.
Bridges to your platform and your catalog
Connectors push governed metadata into your data platform and your business catalog, so the same model is visible to engineers, stewards, and AI tools.
Skill-first by design
Data management veterans encoded the playbook into skills your agents use out of the box.
Ready for change
Schema evolution is handled systematically, preserving business intent while minimizing downstream disruption.
Unified metadata foundation
Flow reuses the same metadata foundation as VaultSpeed Core, ensuring consistency from Silver to Gold.
Platform-neutral
Flow supports multiple cloud platforms and execution engines while avoiding vendor lock-in.
Bring AI agents to your data foundation
Bring AI agents to your data foundation
Bring AI agents to your data foundation