Upsolve AI

Upsolve AI is a platform for building grounded analytics agents that answer business data questions with traceable context for trusted chat or embedded analytics.

AI Analytics Assistant AI Agent Development AI Developer Tools

Visit Website

What Upsolve AI is

Upsolve AI is a platform for building, deploying, and evaluating analytics agents that answer questions against business data. Its core focus is grounding agent output in approved context so responses are more accurate, traceable, and consistent with a team’s definitions and data models.

The product is aimed at data teams and product teams that want to serve trusted answers in chat or embedded experiences. The home page describes a two-sided platform: builders use Agent Studio to encode context, test behavior, and deploy agents; end users ask questions and receive grounded answers with visible lineage and verification details.

Core capabilities

Broad warehouse and connector support

Upsolve’s home page says teams can bring in 30+ SQL database connectors out of the box and import an existing dbt project, while the pricing page says all plans support 50+ data connections.

Context management for governed answers

The platform organizes agent context into structure, meaning, and trust so answers can be tied to tables, metrics, definitions, and approved sources instead of raw prompts alone.

Full conversation observability

The product traces conversations end to end, showing the user question, tool calls, SQL queries, and agent output so teams can inspect how an answer was produced.

Evaluation and context monitoring

Upsolve includes built-in evaluation and monitoring to grade performance, surface missing context, and adapt when metric definitions change.

Analytics delivery and inspection tools

The pricing page says the platform includes charts and visualization, context management, observability and eval, and an MCP app across plans.

Deployment features for product teams

The Team and Enterprise plans add embedding, row-level security or RBAC, multi-tenant support, semantic-layer tooling, and scheduling for AI dashboards and email.

Common ways teams use it

Internal analytics Q&A
Create chat-based analytics agents that answer recurring questions about revenue, churn, pipeline, utilization, or other business metrics using approved definitions and SQL patterns.
Embedded product analytics
Package an analytics assistant inside a customer-facing product with embedding, row-level security, and multi-tenant support on higher-tier plans.
Metric governance and context encoding
Encode business rules, KPI definitions, and other institutional knowledge into the context layer so answers stay aligned with how the team actually measures performance.
Debugging and quality review
Inspect tool calls, SQL, and lineage when an answer looks off, then use evaluation and monitoring to close gaps in the context layer.
Fast-start deployment for data teams
Import an existing dbt project and connect to supported warehouses to move toward a working agent without building a semantic layer from scratch.

Pros and Cons

Pros

Supports a context-driven workflow for analytics agents, including structure, meaning, and trust.
Shows answer lineage and conversation traces so teams can inspect how responses were produced.
Includes evaluation and monitoring to surface gaps and refine context over time.
Offers multiple deployment paths, from a free trial tier to custom Enterprise arrangements.
Provides data-warehouse connectivity and dbt import support without requiring a semantic layer to get started.

Cons

The source does not provide a complete list of integrations or connectors beyond examples and broad connector counts.
Some capabilities are plan-gated, such as embedding, RBAC, multi-tenant support, and deployment options for higher tiers.

FAQ

What is Upsolve AI used for?

Upsolve AI is designed for analytics agents that answer questions against your data with grounded context, validated SQL, and governed business logic. The source emphasizes agent-building, deployment, and evaluation for data teams rather than a general-purpose chat product.

What data sources can it connect to?

The pricing page says Upsolve supports 50+ data connections on all plans, and the home page lists connectors such as Snowflake, BigQuery, Redshift, Postgres, Databricks, and MySQL, with dbt project import supported. The source also says a semantic layer is optional to get started.

Does Upsolve AI offer a free or paid plan?

Yes. The pricing page includes a Free tier, Pro and Team tiers, and a custom Enterprise plan. It also states that annual commitments receive a 20% discount on the base fee.

Can teams embed Upsolve AI into products or internal workflows?

Yes. The pricing page says Team and Enterprise support embedding, while Enterprise adds row-level security or RBAC, multi-tenant support, and dedicated support. The home page also describes a two-sided platform for builders and end users.

How does Upsolve AI help with trust and evaluation?

The source indicates that conversations are traced end to end, including user questions, tool calls, SQL generation, and agent output. It also says the platform includes built-in evaluation and context monitoring to surface gaps and improve accuracy over time.

Quick Facts

Category: AI Analytics Platform
Primary users: Data teams, analytics teams, and product teams
Source domain: upsolve.ai
Pricing model: Free, Pro, Team, and custom Enterprise plans
Notable workflow: Build, test, deploy, and evaluate grounded analytics agents
Connectors: 50+ data connections on pricing page; 30+ SQL connectors out of the box on home page