One definition of ARR. Every agent answer uses it.
Register your data model, metric definitions and business vocabulary once. Upsolve grounds every agent answer in them, versions them like code, and keeps column values fresh as your data changes.
New · Launch Month day 1, Wed 30 Sep 2026 · Upvote on Product Hunt →
Ten definitions of revenue. One agent guessing between them.
Schemas don't say which table is the source of truth or how your team defines a metric.
Prompt-only context drifts the moment a status, term or category changes in the data.
When the agent picks the wrong definition, the number looks right and nobody notices until the board meeting.
From setup to production in three steps
Register your tables
Attach tables and fields to an Upsolve data model, with descriptions, types, and primary and foreign keys.
Encode your vocabulary
Put canonical definitions, analysis approaches and output formats in a versioned system prompt.
Keep it fresh
Mark columns as selectable and Upsolve pre-caches their values, refreshed nightly or on your schedule.
What changes with Upsolve Data Models
Ten definitions of revenue live in docs, Slack and people's heads. The agent picks one and the number looks right.
Tables, keys, descriptions and canonical definitions live in one versioned model the agent always reads.
Everything that ships with Data Models
Descriptive data model
Every table and column carries the context your best analyst would give a new hire.
Versioned like code
System prompts and models have versions and drafts, so you can change context safely and roll back.
Selectable values
The agent already knows your five contract statuses and four payment terms before it writes SQL.
No semantic layer required
Start from your warehouse as it is today. Add definitions as you find the gaps.
One layer of a closed learning loop
Upsolve builds the whole stack for data agents that stay accurate in production. Every conversation feeds the next improvement.
Part of Upsolve Launch Month
Common questions
No. You can build the data model in Upsolve directly on top of your warehouse tables, then add definitions over time.
Prompts hold business practice. The data model holds the truth of the underlying data: tables, keys, descriptions and live column values. Upsolve uses both.
Nightly by default, or on whatever cadence you set, so the agent's picture of your data doesn't drift.
Can't find your answer? Get in touch.