What is Upsolve AI?
Upsolve AI is a set of business intelligence tools built for data teams that need to deploy AI-driven analytics agents. Too many data teams spend their time fielding the same questions over and over. Upsolve lets those teams encode institutional context into AI agents that give verified, trustworthy answers on demand.
The AI platform is built around a three-layer context architecture: structure, meaning, and trust. Data teams define the rules, and end users get self-serve analytics grounded in actual business logic. It fits data analysts, analytics engineers, BI leads, and AI innovation teams at mid-market and enterprise companies.
Upsolve AI Video
Features & Benefits
- Agent Context Studio: Build analytics agents layered with your company’s business intelligence tools, KPI definitions, and validated SQL patterns so answers stay accurate.
- Three-Layer Context Architecture: Encode structure, meaning, and trust into every agent response through warehouse tables, semantic models, and verified definitions.
- Workspaces: Organize each agent around a paired data model and context set, with draft and production versioning built in.
- 30+ SQL Database Connectors: Connect data warehouses including Snowflake, BigQuery, Redshift, Postgres, Databricks, and MySQL with no pre-built semantic layer required.
- dbt Project Import: Bring an existing dbt project directly into the agent setup without rebuilding your models.
- Golden Assets: Add trusted question-and-SQL pairs the agent references via RAG to write correct queries for recurring business intelligence tools questions.
- Golden Charts: Pair questions with specific chart configurations so the agent picks the right visualization type automatically.
- Skills (Beta): Create modular, reusable instruction sets the agent loads on demand when a question matches the skill’s description.
- Knowledge Base: Upload reference documents like policies, definitions, and data caveats the agent searches at query time.
- User Memory (Beta): Let the agent learn each end user’s preferences for chart types, time ranges, and detail levels across conversations.
- System Prompts: Write always-on business rules, KPI definitions, and behavioral guardrails the agent follows on every message.
- Validated SQL Pattern Encoding: Store approved SQL queries so the agent reuses tested logic instead of generating new queries from scratch.
- Conversational Analytics: Let end users ask questions in plain language and get answers grounded in business logic your team already defined.
- Full Agent Tracing and Observability: View every step of an agent’s work, including tool calls, SQL generated, and context layers verified, in a traced timeline.
- Built-In Evaluation Agent: Grade agent performance across multiple criteria and surface context gaps that need fixing.
- Prompt-Based Dashboard Creation: Generate interactive, shareable dashboards from a single prompt, complete with charts and key metrics.
- Embeddable Analytics Layer: Embed dashboards and the chat agent directly into your product with backend auth and multi-tenancy support.
- Multi-Tenancy and Row-Level Security: Scope data access per tenant so each end user only sees their own records.
- Multi-Surface Deployment: Deploy agents to Slack, Teams, Claude, ChatGPT, Cursor, MCP, or embed them in your own product via SDK.
- MCP as Context Source: Connect external tools and live systems as additional context the agent can pull from.
- Behavioral Guardrails: Set rules and definitions that restrict how the agent responds so it stays within approved boundaries.
- Workspace Versioning: Draft, test, and publish agent configurations so edits never reach production until you promote them.
- Dashboard Sharing and Collaboration: Share dashboards via email and let team members view, edit, and collaborate.
- Context Layer Healing (Coming Soon): Let AI detect and fix gaps in the context layer automatically as metric definitions change.
What can Upsolve AI do?
- Build AI analytics agents
- Query data with natural language
- Create dashboards with a prompt
- Verify KPIs against business rules
- Encode semantic context for data agents
- Trace AI agent tool calls and SQL
- Deploy analytics agents to Slack
- Evaluate AI agent accuracy
- Embed analytics into SaaS products
- Connect SQL databases to AI agents
- Import dbt projects into analytics workflows
- Store reusable analysis procedures as skills
- Remember end user chart preferences
Real-World Applications
If you run a data team that keeps getting buried under ad-hoc requests, Upsolve AI can cut that backlog down fast. You encode your definitions once, and the agent fields repeat questions on its own. Your analysts get time back for deeper work.
A SaaS company’s finance team might use these business intelligence tools to check churn rates or MRR breakdowns without filing a ticket. The agent pulls from validated SQL and applies the company’s own KPI definitions, so the numbers match what leadership expects.
Sales and revenue operations teams can ask about pipeline coverage, quota attainment, or deal velocity in plain language. The agent applies guardrails your data team set, so reps get answers they can act on without second-guessing the math.
For heads of AI or chief data officers trying to scale analytics across departments, Upsolve offers a way to deploy business intelligence tools that improve over time. Every conversation feeds back into the system, surfacing context gaps the team can fix. Skills and user memory let the agent get smarter with use, not just with manual tuning.
SaaS companies that sell to other businesses can embed the analytics layer directly into their product. Multi-tenancy and row-level security keep each customer’s data separate. End users get a chat interface and dashboards scoped to their own records.
