Start with a workflow, not a model
A useful AI project begins with a decision, an owner and a repeatable piece of work.
By Alex Vale · September 22, 2026
Big ideas. Working 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.
TokenMonster is an AI consulting company built around one idea: intelligence matters when people can use it. We connect data foundations, analytics and application engineering to help teams move from a promising experiment to a service they understand and can operate.
We start with a workflow, define what a good result looks like, and build against that standard. The work includes the less glamorous parts: permissions, evaluation, deployment and a clear handoff.
A useful AI project begins with a decision, an owner and a repeatable piece of work.
By Alex Vale · September 22, 2026
Reliable answers depend on definitions, freshness and permissions as much as the interface.
By Noor Stone · September 21, 2026
Separate task success, mistakes, time and cost before you build a leaderboard.
By Alex Vale · September 20, 2026
Assign responsibility before the pilot starts.
By Noor Stone · September 19, 2026
Make it possible to inspect the evidence behind an answer.
By Alex Vale · September 18, 2026
Completion should be observable, and retries should have limits.
By Noor Stone · September 17, 2026
When automation cannot finish, preserve the context for a person.
By Alex Vale · September 16, 2026
The interface and the backend must agree on who can do what.
By Noor Stone · September 15, 2026
Deployment, support and handoff turn an experiment into a service.
By Alex Vale · September 14, 2026
Keep inputs and evaluation consistent when choosing a model.
By Noor Stone · September 13, 2026