Start with a workflow, not a model
A useful AI project begins with a decision, an owner and a repeatable piece of work.
THE PRACTICAL SIDE OF AI
We turn complicated data and everyday workflows into useful AI systems. Explore practical field notes on strategy, analytics, agents and the work of getting them into production.

THE PUBLICATION
Ideas for building AI
that earns its place in the workflow.
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.
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.
The interface and the backend must agree on who can do what.
Deployment, support and handoff turn an experiment into a service.
Keep inputs and evaluation consistent when choosing a model.
CURIOUS. CAPABLE. GROUNDED.
TokenMonster connects data foundations, analytics and application engineering to help teams move from a promising experiment to a service they understand and can operate.
Start with one useful workflow ↗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 and help the team own what comes next.