Intelligence is no longer the bottleneck.

With appropriate supervision, it's clear that current and future models can autonomously create incredible things. We build tools and systems for higher abstractions of human supervision in agentic engineering and agentic data analysis.

See the results

Results

Already at work for real customers.

$1B+

in actionable insights found by customers since 2021

$100M+

in estimated value from insights AT&T put into action

$58M

found for a Fortune 500 healthcare payer in six months

65.7%

fewer hallucinations than peer tools in natural-language data queries

DA-Bench

36%

fewer zero-score failures when the same agent runs DeepWork workflows

AgentIF-OneDay

2.4×

tighter run-to-run consistency for the same agent on DeepWork workflows

AgentIF-OneDay

One run, from its own logs: kicked off at 11:24 PM, searched 194 million rows overnight, got its draft bounced by a quality gate, and had a human-approved report by 6:05 AM.

06:24:20job acceptedanalyze medicaid FFS spending · scope: 8 questions
06:43:48analysis done4 parallel agents · external sources verified
06:44:44data exported194,002,693 rows (2018–2023) → parquet
07:30:24patterns found9,588 candidates · 2,174s · 30 workers
12:37:22patterns ranked300 kept by KS score · 0 errors, 0 warnings
12:58:38quality gatereport drafted · gate passed on attempt 2
13:04:57approveduser approved the report · workflow complete

Actual event log · timestamps in UTC · Unsupervised.com — March 2026

Finder for Teams

See it in action.

This is where we learned to control agents in production. It runs against warehouse data: KPI-linked patterns, ranked by estimated financial impact, with evidence an analyst reviews before anyone acts.

Named customer · verified

“Unsupervised’s AI Data Analysts have delivered strong ROI—improving our key metrics while empowering our team with faster, smarter access to data insights.”

Mark Austin · VP Data Science, AT&T

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