Descriptive data models
Attach warehouse tables and fields to a model with descriptions, data types, and primary and foreign keys, giving the agent explicit structural context.
Upsolve Data Models
Upsolve Data Models gives AI analytics agents structured context for warehouse tables, keys, metric definitions and business vocabulary. It helps data teams standardize agent-generated answers without requiring dbt or an existing semantic layer.
Upsolve Data Models
Upsolve Data Models is a context and governance layer for AI analytics agents. It links warehouse tables and fields with descriptions, data types, primary and foreign keys, canonical metric definitions, analysis approaches and output formats so an agent has one maintained definition of business data.
Models and system prompts are versioned like code, with drafts and rollback support for context changes. Selectable column values are pre-cached and refreshed nightly by default or on a chosen schedule, helping the agent use current business categories and statuses when generating answers.
The feature is designed for teams that want to start from existing warehouse tables rather than adopt dbt or another semantic layer first. It is one layer of Upsolve's wider platform, which also includes data connections, analytics agents, observability and evaluation capabilities; the exact operational workflow beyond model registration is not fully documented in the supplied source.
Core capabilities
Business vocabulary and definitions
Store canonical metric definitions, analysis approaches and output formats in a versioned system prompt alongside the data model.
Versioned context
Create drafts and versions for system prompts and models, making it possible to change context and roll back without replacing the active definition immediately.
Fresh selectable values
Mark columns as selectable so Upsolve pre-caches their values, with nightly refresh by default or a schedule chosen by the user.
Warehouse-first setup
Build the model directly on warehouse tables without requiring dbt or an existing semantic layer, then add definitions as gaps are discovered.
Practical use cases
Standardize KPI answers
Document ARR, revenue or other KPI definitions alongside the tables and keys needed to calculate them, reducing ambiguity when an agent answers recurring business questions.
Keep business vocabulary consistent
Encode contract statuses, payment terms and other categorical business vocabulary as selectable values so generated analysis uses the organization's terminology.
Improve an existing warehouse incrementally
Start with a warehouse's current schema and add descriptions or definitions only where agents encounter gaps, rather than redesigning the stack around a new semantic layer.
Manage context changes safely
Use drafts and versions to test changes to prompts or model context, then roll back when a definition needs to be revised.
Pros and Cons
Pros
- Defines tables, keys, metrics and business vocabulary in one maintained model for agent use.
- Supports drafts, versions and rollback for safer context changes.
- Works directly with existing warehouse tables without requiring dbt or a separate semantic layer.
- Refreshes selectable column values nightly by default or on a user-defined schedule.
Cons
- The supplied documentation focuses on model registration and context management; it provides limited detail about onboarding steps, outputs and day-to-day administration beyond the documented workflow.
- Specific connector and connection-type availability is not detailed on the Data Models page, although Upsolve states that its platform supports 50+ data connections.
FAQ
Do I need dbt or an existing semantic layer?
No. You can build a data model directly in Upsolve on top of existing warehouse tables and add definitions over time; dbt or another semantic layer is not required.
How is this different from a long system prompt?
A system prompt stores business practices and instructions, while the data model stores the underlying data context: tables, keys, descriptions, canonical definitions and live column values. Upsolve uses both.
How often are cached column values refreshed?
Values are refreshed nightly by default, or on a cadence selected by the user, so the agent's view of selectable column values stays current.
What does Upsolve Data Models work with?
Data Models can be used with Upsolve's broader analytics-agent platform. The pricing page lists 50+ data connections across plans, while specific connection availability and workflow details may vary by configuration.
Quick Facts
- Category
- AI analytics agent context management
- Primary users
- Data teams building and maintaining AI analytics agents
- Setup model
- Attach warehouse tables and fields, then add definitions and vocabulary
- Semantic layer requirement
- No dbt or existing semantic layer required
- Value refresh
- Nightly by default or on a user-defined cadence
- Platform
- Upsolve cloud analytics platform
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