Upsolve AI is the platform for deploying governed, grounded, and trustworthy AI data agents to your internal teams or external customers. It helps data teams move past the broken self-serve BI promise by combining the conversational power of large language models with the rigor of a robust semantic layer, rich business context, and built-in evaluation and observability to put agent and context improvements on a flywheel. End users get accurate answers while the data team stays in control.
The platform has two complementary components: the Upsolve Data Agent and the Upsolve Agent Context Studio.
1. The Upsolve Data Agent. The Upsolve Data Agent does what a good data analyst does. It answers business questions in natural language, asks clarifying questions when a request is ambiguous, generates charts and reports on the fly, and helps users explore the underlying data without writing SQL. These analyst behaviors come built into the agent itself. What the Upsolve Data Agent does:
Conversational, natural-language Q&A over your data, with built-in text-to-SQL
Multi-step reasoning across datasets, generate interactive charts on-demand(30+ chart types: bar, line, pie, scatter, heatmap, funnel, and more), auto-generated reports and summaries tailored to a user's role (exec, PM, ops, support, finance), conversational drill-down and follow-up turns in the same thread.
The Upsolve Data Agent is designed to be easily customizable. We don't believe in an out-of-the-box data agent. Data agents are only useful and reliable with rich context and when assessed against your own performance benchmarks. A generic LLM connected to a database produces generic answers and hallucinated metrics. To produce true, traceable, and transparent answers about your business, the agent needs three more things, and that is where the Data Agent Studio comes in.
2. Upsolve Data Agent Studio. The Data Agent Studio is the place for your team to make the Data Agent accurate, reliable, and trustworthy. It is built on three layers: Data Layer, Context Layer, and Trust and Evals Layer.
Data Layer: OOTB database connectors + Cockpit for Semantic Layer construction
Database connectors for ClickHouse, Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL, and other major data warehouses. Robust data governance with Multi-tenant architecture with row- and column-level permissioning. For teams that don't have a unified semantic layer model, the Cockpit tool provides a guided and guardrailed interface that takes your team from fragmented data to a clean, AI-ready semantic layer in a week, instead of the six to eight months a typical V1 takes.
Context Layer: your data agent needs a unified, auto-curated, and auto-refreshed context layer.
The Context Layer connects your files, docs, and systems (Notion, Confluence, Google Drive, internal wikis, product release notes, dbt files, data catalogs, metric glossaries) and curates the information the agent needs to understand the business behind the numbers. It captures product launches, definition changes, ontology, naming conventions, and team-specific business logic so a query like "usage spiked last week" turn into "usage spiked last week, and this correlates with Tuesday's Pro plan launch". The Context Layer scans for updates across systems of records connected to it, flags context gaps, and suggests updated. Humans always in the loop.
Trust and Evals Layer: Testing, end-of-end observability, and evaluations according to your benchmarks.
Sandbox environment for pre-deployment testing, plus production logs after launch. Full LLM observability: every prompt, tool call, model used, retrieved context, and reasoning step is logged, traceable, and reviewable. Built-in evaluations: define gold-standard queries and answers, run tests, and catch agent drift with in-built LLM-as-a-judge evaluators before stakeholders. This system then creates a self-improving feedback loop when corrections from your team feed back into the agent's eval set so quality compounds over time.
Together, those three Data Agent Studio components turn a generic agent into one that produces accurate, reliable, and trustworthy answers your team can defend.
Delivery and deployment:
The Upsolve Agentic Dashboard is a conversation-driven exploration surface for end users, with 30+ chart types, interactive filters, drill-down, PDF export, scheduled email, and version history. End users can save generated charts to a personal dashboard or simply tell the agent their role and have it auto-build a full dashboard for that persona, solving the blank-page problem typical of self-serve BI tools. SSO, role-based access control, and audit logging are included.
The same Data Agent you customized can be accessed by your users wherever they already work. Embedded inside your own product as customer-facing analytics for B2B SaaS as an iFrame or React component; deploy into Slack and Microsoft Teams as in-channel data bot; access in Claude, ChatGPT, Cursor, and any MCP-compatible client to get data while you work and stage multi-agent workflows; or any custom application via REST API.
Common use cases:
Internal self-service business intelligence for data teams that don't want to be a ticket queue. Customer-facing embedded analytics for B2B SaaS products. Conversational analytics, generative BI (GenBI), and agentic BI deployments
AI-powered reporting and operational analytics for ops, finance, support, and product.
Multi-tenant SaaS analytics with row-level security. Looker, Tableau, Power BI alternatives for teams that want an AI-native approach.
Why Upsolve AI
AI-native architecture. Upsolve was built around the agent from day one, not retrofitted onto a legacy BI tool.
Data team in control. The semantic layer, business context, and evals all live with your team, not buried inside a black-box LLM.
Fast time-to-value. Cockpit takes you from fragmented data to a working semantic layer in a week.
Real evaluation. Most "AI for BI" tools skip evals. Upsolve makes them first-class.
Build in Studio, access anywhere. The same agent across internal dashboards, Slack, Teams, your product, and MCP-compatible clients.
Enterprise-ready. SSO, role-based access control, multi-tenant governance, audit logging, and full observability included.
Built for modern data and AI stacksUpsolve plugs into Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL. It works alongside dbt, existing semantic layers, and your data catalog. The Data Agent can be invoked from Claude, ChatGPT, Cursor, or any MCP-compatible client, making it easy to compose multi-agent workflows where Upsolve handles the data analysis step.
YC W24.
Average Rating: 4.8/5.0
Total Reviews: 16
How Do G2 Users Rate Upsolve AI?
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Has the product been a good partner in doing business?: 10.0/10 (Category avg: 8.9/10)
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Steps to Answer: 8.6/10 (Category avg: 8.3/10)
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Reports Interface: 9.0/10 (Category avg: 8.8/10)
Who Is the Company Behind Upsolve AI?
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Seller: Upsolve AI
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Company Website:
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Year Founded: 2023
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HQ Location: London, GB
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LinkedIn® Page: www.linkedin.com
4 employees on LinkedIn®
Who Uses This Product?
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Company Size: 56% Small, 31% Medium
What Do G2 Reviewers Say About Upsolve AI?
AI-generated summary from verified user reviews
Pros
- Users find Upsolve AI's ease of use exceptional, simplifying dashboard creation and enhancing overall workflow efficiency.
- Users value the ease of setup with Upsolve AI, which simplifies dashboard creation and enhances workflow efficiency.
- Users commend Upsolve AI’s excellent customer support, appreciating their reliability and consistent problem-solving assistance.
- Users value the super fast insights and analytics of Upsolve AI for effective customer success communication.
- Users value the time-saving capabilities of Upsolve AI, drastically reducing data compilation time and enhancing decision-making.
Cons
- Users find the limited options for customization and design tools hinder their ability to create effective dashboards.
- Users face usage limitations with Upsolve AI, experiencing slow chart loading and restricted design customization options.
- Users note the complexity of analysis in Upsolve AI, requiring multiple iterations for desired results.
- Users note a significant learning curve in configuring the UI due to numerous options and features.
- Users find the learning difficulty challenging due to the multitude of configuration options requiring extensive understanding.
