Evidence before action.

Statistical search across your whole dataset, ranked into evidence an agent can act on and a human can audit.

$ npm install -g @ai-outfitter/outfittercopy install then pick the Data Analyst profile

See a published run →

194 million rows. One overnight run.

From the published HHS Medicaid analysis: the finder's own event log. The report's highest-scoring pattern: a segment of organizationally complex, high-volume, low-cost services with mean spending growth of 179,156% — explosive growth from small bases, delivered with the segment conditions attached.

06:53:47  data loaded      194,002,693 rows · 10.33 GB · 17.5s
06:54:10  search started   KPI: TOTAL_PAID · 40 conditions · depth 5
07:30:24  patterns found   9,588 candidates · 2,174s · 30 workers
12:37:22  patterns ranked  300 kept by KS score · 0 errors, 0 warnings

How it works

01

Search, don’t guess

An agent exploring by SQL tests one hypothesis per query. It only finds what someone thought to ask. Finder searches the space itself: 100x more data at 1/100th the cost of query-by-query exploration.

02

Ranked, with receipts

Every pattern arrives ranked by statistical strength, with the segment conditions visible — never a black box. Caveats the system found are recorded alongside.

03

Built for agents

The output is evidence an agent can act on: ranked candidates, open questions, and a trail a human can audit before anything ships.

Quickstart

Run it on your own data today.

Try it in Pi

$ npm install -g @ai-outfitter/outfitter
$ outfitter                    # pick the Data Analyst profile

Try it in Claude Code

$ curl -fsSL https://unsupervised.com/cli/install.sh | sh
$ unsupervised finder --help