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We turn complicated data and everyday workflows into useful AI systems. Explore practical field notes on strategy, analytics, agents and getting them into production.
Clear thinking for AI that has to work in the real world.
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ev-f3c8ca-b087bc9a needle-excerpt
ev-f3c8ca-b087bc9a needle-excerpt
A useful AI project begins with a decision, an owner and a repeatable piece of work.
Reliable answers depend on definitions, freshness and permissions as much as the interface.
Separate task success, mistakes, time and cost before you build a leaderboard.
Assign responsibility before the pilot starts.
Make it possible to inspect the evidence behind an answer.
Completion should be observable, and retries should have limits.
When automation cannot finish, preserve the context for a person.
We connect data foundations, analytics and application engineering to help teams move from a promising experiment to a service they understand and can operate.
Choose a meaningful workflow and a measurable first release.
Connect the information, definitions and permissions behind a useful answer.
Build software that assists, explains and acts within clear boundaries.
Test the complete experience, deploy it and help the team own what comes next.
A little less monster. A lot more momentum.