Start with a workflow, not a model
A useful AI project begins with a decision, an owner and a repeatable piece of work.
Big ideas. Working AI.
TokenMonster Insights
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.
What we help teams do
Strategy & scope
Choose a meaningful workflow and a measurable first release.
Data & analytics
Connect the information, definitions and permissions behind a useful answer.
Agents & applications
Build software that assists, explains and acts within clear boundaries.
Delivery & adoption
Test the complete experience, deploy it and help the team own what comes next.
Meet the monster
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.
Field notes
Your data is part of the product
Reliable answers depend on definitions, freshness and permissions as much as the interface.
What to measure before you launch an agent
Separate task success, mistakes, time and cost before you build a leaderboard.
A small pilot needs an owner
Assign responsibility before the pilot starts.
Source links are a product feature
Make it possible to inspect the evidence behind an answer.
Give agents a clear stopping point
Completion should be observable, and retries should have limits.
Make the fallback useful
When automation cannot finish, preserve the context for a person.
Permissions belong in the workflow
The interface and the backend must agree on who can do what.
A demo is the beginning of delivery
Deployment, support and handoff turn an experiment into a service.
Compare models on the same work
Keep inputs and evaluation consistent when choosing a model.