Upsolve AI

Upsolve AI is an analytics agent platform purpose-built for data teams that want to deliver trustworthy, context-rich insights across their organization. It provides an Agent Studio where builders encode institutional knowledge, allowing anyone to ask questions and get verified answers without waiting in line for an analyst. The platform's core value lies in eliminating AI hallucinations by grounding every output in the business's own definitions, SQL patterns, and semantic models. This is not another chatbot; it is a production-grade system designed for teams that need to scale their analytics output reliably.

Every data team faces a relentless queue of repeat questions—47% of requests are duplicates, according to the site. Analysts spend weeks on tickets that could be automated, while business users wait 2–4 weeks for simple metrics like pipeline or churn rate. Generic AI tools produce demos that look correct but fail in production because they lack the business context encoded in internal documents, Slack conversations, and meeting notes. Upsolve AI directly addresses this by structuring the messy institutional context that makes data meaningful, turning scattered knowledge into a reliable resource.

Upsolve AI's three-layered Context Architecture is the foundation of its reliability. The first layer, Structure, ingests warehouse tables and their relationships directly from databases like Snowflake, BigQuery, Redshift, Postgres, Databricks, and MySQL, plus imports dbt projects. The second layer, Meaning, defines metrics, dimensions, and definitions in semantic models to create a consistent vocabulary. The third layer, Trust, verifies answers against golden sources and usage signals, ensuring every response is KPI-verified, SQL-matched, and definition-applied. This encodes what generic AI is missing, providing a skeleton, vocabulary, and judgment for every query.

The platform provides an observable agent-building studio where every conversation is traced end-to-end. Builders can see each tool call, SQL query, LLM invocation, and context verification step with precise timing and token counts. This opens the black box so agent behavior is 100% transparent. The test and deploy workflow allows teams to validate agent responses before releasing them to end users, with built-in evaluation agents that grade performance on multiple criteria and surface context gaps automatically. Context monitoring adapts as definitions change, hardening output relevance and correctness over time.

Upsolve AI meets users where they already work by deploying analytics agents to Slack, Microsoft Teams, Claude, ChatGPT, Cursor, and via MCP for embedding in any product. The same context architecture and guardrails apply across every surface, ensuring consistent answers. End users can also create fully interactive, shareable personal dashboards with a simple prompt. This eliminates the need for traditional BI tool training, letting anyone generate charts and tables on demand based on verified data. The platform supports embedding via SDK and has 30+ SQL database connectors out of the box.