Unsupervised is an enterprise AI analytics platform that automatically finds patterns in large datasets and turns them into plain-language insight reports. Instead of waiting for analysts to form hypotheses, its agents search the data directly — in one published example, scanning 194 million rows overnight — and surface ranked, quality-checked findings for human approval.
The platform runs automated analysis workflows in which multiple agents explore data in parallel, verify findings against external sources, and draft reports that pass through quality gates before a person approves them. Unsupervised reports more than $1B in actionable insights found by customers since 2021, including over $100M in estimated value from insights AT&T put into action and $58M found for a Fortune 500 healthcare payer in six months. The company also publishes benchmark results highlighting fewer hallucinations in natural-language data queries compared with peer tools.
The platform’s workflows, which the company refers to as DeepWork, are benchmarked publicly: Unsupervised cites 36% fewer zero-score failures and 2.4 times tighter run-to-run consistency when the same agent runs DeepWork workflows on the AgentIF-OneDay benchmark, alongside DA-Bench results for its natural-language querying. In the published run log, a single overnight job analyzed Medicaid fee-for-service spending across eight questions, exported more than 194 million rows, generated thousands of candidate patterns, kept the top 300 by statistical score, and delivered a human-approved report by early morning — illustrating the scale and auditability the platform is designed for.
Unsupervised is a Paid enterprise platform. Engagements are arranged through demos and direct sales, and there is no public free tier.
Unsupervised is built for large enterprises — such as telecom, healthcare, and insurance organizations — whose data is too large for manual exploration and that need a steady stream of validated, decision-ready insights.