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## About

NameStop Token
PositioningCommon sense AI agency
PeopleMax Ivanov, Alex Barakov
ModelTwo senior practitioners. Whoever you meet on the first call does the work.

Most AI projects fail from too much, not too little. We find the smallest system that moves a number you already report on, build it, and stop. Two of the four services exist to stop a client spending.

## Services (4)

AI Readiness audit

PromiseWhere AI pays for itself here, and which of it you should not buy.
ForAI / Data office, BI teams, CTO or head of data — the people holding the AI budget
Duration4–6 weeks
When not to buyIf the decision is already made and funded, this is an expensive way to confirm it. Start with AI of Common Sense.
OutcomeA ranked list of candidates with the numbers under each, a written case for the ones worth funding and against the ones that are not, and a map of the gaps that have to close before any of it starts.

Topics

  • current AI and analytics usage: who uses what, how often, and to what end
  • workflows ranked by hours returned against cost to build and to run
  • data and access readiness per candidate: what blocks each one and for how long
  • tooling review: overlap, shelfware, seat licences nobody opens
  • vendor lock-in exposure and the cost of leaving each proposed stack
  • skills and ownership: who would run this after it ships
  • governance, security and the approvals a production system will meet
  • an honest reading of what your team can absorb at once

AI Transformation Planning

PromiseWhat to build over the next twelve months, and what to refuse.
ForExecutive team and the CDO or CTO office
Duration6–10 weeks
When not to buyIf the spend has not started yet, the readiness audit is cheaper and answers more of the same questions.
OutcomeA twelve-month roadmap with sequenced bets, the staffing and infrastructure behind each, the metric tree that will show whether it worked, and the list of what the company has decided not to build.

Topics

  • target operating model: what stays central, what moves into the teams
  • initiative portfolio: sequencing, dependencies, and what each one needs to start
  • the economics: hours returned, cost to run, and an ROI model with named assumptions
  • measurement design built into the reporting you already run, not into a slide
  • roles and skills: who you hire, who you train, who you stop hiring
  • platform decisions: build, buy, or wait, with the reasoning written down
  • governance and risk appetite for agents acting on company systems
  • a refusal list: the bets declined, and why

AI of Common Sense

PromiseThe smallest set of tooling and process that lets your team go on without us.
ForAI / Data office, BI teams, and the engineering leadership that has to own the result
Duration8–12 weeks
When not to buyIf nobody on your side can own this after we go, buy an adoption marathon first. A handover with no one to receive it is a shutdown with extra steps.
OutcomeA running set-up inside your infrastructure, the evaluations that prove it works, a skill library your team owns, and a runbook saying what it costs a month, where it fails and what to watch. Then we leave.

Topics

  • skills: a library your team writes and extends, not a pile of prompts
  • MCP services exposing your own systems, with access scoped and audited
  • an LLM gateway: one key, budgets, fallbacks, and a bill you can read
  • the harness: how agents are run, retried, sandboxed and observed
  • context management: what an agent is allowed to know, and where that lives
  • evaluations: a labelled set and a score that moves on every change
  • duty bots: the on-call and triage work agents can take off people
  • data assistants built on your semantic layer rather than raw tables
  • JTBD mapping: which jobs are worth an agent and which are not

AI Adoption projects

PromiseYour teams using AI on their own work, in cohorts, with the hours measured.
ForAI / Data office, BI teams, and the leader whose licences are bought but whose usage is flat
Duration4–6 weeks per marathon
When not to buyIf nobody can yet say which work AI should touch, start with the readiness audit. A marathon without a target is a training course.
OutcomeSomething shipped into the cohort's weekly routine, hours returned measured per participant, a written standard for how this team builds and reviews AI work, and facilitators for the next cohort who already work for you.

Topics

  • cohort selection: the roles where the hours are, not the volunteers
  • baseline: how long the work takes today, measured rather than estimated
  • working sessions on the team's own tools, data and backlog
  • the patterns that fit their job, and the ones that will waste their time
  • review practice: how the team checks AI work before it counts
  • an internal standard the cohort writes and then has to live with
  • facilitators trained from inside, so the next marathon is yours
  • what to do with the people the change makes uncomfortable

## How we work — ENOUGH

  • E — Examine. What is already true: readiness, the jobs to be done, what has been bought
  • N — Narrow. What to build, in what order, and what will not be built
  • O — Outline. The smallest architecture that carries the chosen work
  • U — Use. One or two things in production, used in real weeks
  • G — Gauge. What it returned and what it costs to run
  • H — Hand over. The roadmap the team runs without us

## Stack map

Tools117
Layers16
Builds7
Open source70
Full listing/m/stack

## Contact

Emailinfo@stop-token.com
WhatsApp+31 6 81116176
Telegram@alexbarakov
AddressVrouw Rijssensloot 32, 2614 MA Delft

2026 Data Nature · KVK 90218426 · Common sense AI agency