Upsolve AI
Upsolve AI · Coding · Marketing
Upsolve AI is an agent studio for data teams. Encode business logic once, then ship analytics agents that answer questions in Slack, Claude, or your own app.
About Upsolve AI
What Is Upsolve AI
Upsolve AI is a platform for building AI data analysts that know how your company defines its numbers. You connect a warehouse, pick the tables that matter, write down what each metric means and which rules apply, and the agent answers business questions using that context instead of guessing from column names.
The product is split in two. Data people work in Agent Studio, where each workspace holds one data model and one agent, versioned together. Everyone else gets a chat interface plus shareable dashboards, and they never see a SQL prompt. That split is the whole point. The people who understand the data stay in control of definitions, and the people who need numbers stop filing tickets.
Three limits shape who this fits. It assumes you have a real warehouse with clean-ish tables, since the context you encode is only as good as what it points at. It's priced for teams, with the cheapest paid plan at $500 per month. And the accuracy story depends on maintenance: when a metric definition changes, someone has to update the workspace. Upsolve watches for those gaps, but a human still closes them.
Getting Started
- Create an account at ai-hub.upsolve.ai. The free tier gives you 2,000 one-time credits and no card is required.
- Connect a data source. Upsolve ships connectors for Snowflake, BigQuery, Redshift, Postgres, Databricks, MySQL, and more than 30 other SQL databases. You can import an existing dbt project if you have one.
- Build the data model. Choose which tables and columns the agent can see, then annotate them with business meaning so it reads
arr_usdas annual recurring revenue, not as a random decimal. - Encode the rules. Add KPI definitions, guardrails, and golden assets, which are example queries and charts the agent reuses for common question patterns.
- Test and deploy. Ask the agent real questions, review the traces, fix what it gets wrong, then publish it to Slack, Teams, Claude, ChatGPT, or your own product through the embed SDK.
Product Information
A quick look at Upsolve AI's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Data analysts and analytics engineers
- Sales, finance, and operations leads
- Product teams that sell analytics to their own customers
Tasks
- Answering repeat questions
- Checking a number before a meeting
- Turning an answer into a dashboard
- Giving an AI assistant access to governed data
Scenarios
- A weekly business review where three people ask for the same churn number
- Onboarding a new analyst
- Shipping an analytics panel inside a SaaS product
Key features
Context Layer With Data Models
The data model page decides what the agent can see: specific tables, specific columns, and annotations that explain what they mean. You don't need a pre-built semantic layer to start. Importing a dbt project keeps the definitions you already trust. This is the layer generic chatbots skip, and it's the reason an answer can cite a KPI instead of a lucky join.
Golden Assets and Golden Questions
Golden assets are approved queries and charts tied to common question patterns. Golden questions go further: you record the correct answer so the agent has a reference point, and it can flag when it isn't sure. In practice this turns tribal knowledge into something testable, which matters when the person who wrote the original SQL leaves.
Observability and Live Evals
Every conversation is traced from the user's question through tool calls and generated SQL to the final answer. An evaluation agent grades responses against your criteria, and context monitoring surfaces the gaps that caused a bad answer. Harness 2.0, the current agent runtime, is billed as twice as fast as the previous version, which changes how the chat feels on a long analytical question.
Multi-Surface Deployment
The same agent runs in Slack, Microsoft Teams, Claude, ChatGPT, and Cursor, and it can be embedded in your own product through an SDK. Context and guardrails travel with it, so a sales rep in Slack and a developer in Cursor get the same definition of revenue. That consistency is harder to achieve than it sounds when each surface has its own prompt conventions.
BI Canvases and Branded Apps
Charts generated in a conversation can be pinned to a shareable canvas, and any canvas can become a branded HTML app in a few clicks. For teams that already pay for a BI tool, this won't replace it. For teams that just need the answer plus a trend line, it removes a step between the question and the screenshot.
Row-Level Security and Multi-Tenancy
On the Team plan, permissions are enforced below the agent, so a user can't talk their way into another customer's rows. Multi-tenant support and an embeddable frontend turn the same workspace into a customer-facing embedded analytics feature. Enterprise adds on-premises or VPC deployment, SAML SSO, HIPAA support, and bring-your-own-model routing.
Pros and cons
Pros
- The context model is the product, not an afterthought: KPI definitions, business rules, and example answers all live in one versioned workspace.
- Full traceability on every answer, including the SQL that ran and the definition that was applied.
- Wide deployment reach for a young platform, with Slack, Teams, Claude, ChatGPT, Cursor, MCP, and embedded SDK support.
- A real free tier with 2,000 credits, enough for roughly 200 analytical questions, and no credit card at signup.
Cons
- The $500 per month entry price puts it out of reach for solo users and small teams that just want a chatbot over their database.
- Setup is real work. Someone has to document metrics and rules before answers become reliable, which can take days rather than minutes.
- The free tier is a one-time credit pool, not a permanent free plan, so evaluation has a hard end.
- No native mobile app, which matters if executives expect answers on a phone outside Slack.
Frequently asked questions
It builds AI analytics agents that answer business questions using your own data model, KPI definitions, and rules. You configure the context in Agent Studio, then expose the agent through chat, dashboards, Slack, or an embedded interface.
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