The problem every data team hits
Your KPIs are documented in Notion. Your business logic lives in a SQL file from 2021. Your team has spent years building institutional knowledge about what the numbers actually mean — what counts as “revenue,” which user segments to exclude, when to apply which filter. Generic AI tools know none of it. Connect them to your database and they’ll generate SQL — but not the right SQL for your specific data model, with your specific business rules applied correctly. The answers look right, but aren’t. And that destroys trust faster than having no AI at all.What Upsolve is
Upsolve is where data teams encode that institutional knowledge into a reliable analytics agent, then expose it to the whole business. It’s a two-sided platform. On one side, data experts use Agent Studio to structure everything the agent needs to know — organized into workspaces, each pairing a curated data model with the agent built on it: which tables matter, what your KPIs actually mean, examples of correct answers to common questions, and rules for who sees what data. On the other side, business users get an analytics interface that answers their questions using that full context — accurately, and without involving the data team every time.What a Workspace is
A workspace is where you build one analytics agent. It’s organized around the idea that a reliable agent isn’t just a model connected to a database — it’s a model that has been deliberately taught your business: Encode your context — The workspace’s Data Model page defines which tables and columns the agent can see; its other pages annotate them with business meaning, write the rules the agent should always follow, and give it examples of how your team actually answers common questions. This is the core work, and it’s what makes the agent trustworthy rather than just capable. Deploy and expose — Once the agent is configured and tested, deploy it to your product. Upsolve handles multi-tenancy, row-level security, and the embedding layer so every user sees only their own data through a clean analytics interface. Improve over time — Every question the agent handles shows you where its context is complete and where it’s missing. The feedback loop is built in: gaps surface as eval failures, and fixing them means adding more context, not retraining a model.Get started
Workspaces
One data model and one agent, versioned and published together — the surface where you build.
Complete Setup Guide
End-to-end walkthrough from first connection to production deployment.
Encoding your context
The three pages below are the core of Agent Studio. Work through them in order when building a new workspace.Data Model & Schema
Choose which tables and columns the agent can see, and annotate them with business meaning.
System Prompts
Encode business rules, KPI definitions, and behavioral guardrails.
Golden Assets
Add example queries and charts the agent references for common question patterns.
Deploying to your users
Analytics Layer
The embeddable dashboard and chart interface that delivers your agent’s output to end users.
Deploy & Expose
Embed your agent natively in your product with backend auth and frontend setup guides.