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/outfitterthen pick the Data Analyst profileArtifact
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.
run · hhs-medicaid · finder events
06:53:47 data loaded 194,002,693 rows · 10.33 GB · 17.5s06:54:10 search started KPI: TOTAL_PAID · 40 conditions · depth 507:30:24 patterns found 9,588 candidates · 2,174s · 30 workers12:37:22 patterns ranked 300 kept by KS score · 0 errors, 0 warnings
How it works
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.
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.
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
terminal
$ npm install -g @ai-outfitter/outfitter$ outfitter # pick the Data Analyst profile
The Data Analyst profile bundles the Finder workflows and sets up the Finder binary the first time you ask for a pattern search.
Try it in Claude Code
terminal
$ curl -fsSL https://unsupervised.com/cli/install.sh | sh$ unsupervised finder --help
For warehouse-connected, team-scale use, see Finder for Teams.