Problems we help solve
What usually brings teams to us
- Engineers use different tools with no shared policy on code or data.
- Nobody knows where prompts containing source code are processed.
- Costs are unpredictable and not attributed to teams.
- Productivity claims are anecdotal, so investment decisions are hard.
Typical situations and decisions
Situations
- Standardising on one or two AI coding tools
- Routing coding assistants through EU inference
- Setting budgets and quotas per team
- Training teams on effective workflows
Decisions we help you make
- Which tools and models, for which work
- Direct provider, cloud provider or gateway
- What code and data may be sent to models
- How to measure impact honestly
How we work
1.
Review current tool use, policies and spend.2.
Recommend tools, routing and controls that fit your delivery process.3.
Set up guardrails and enablement with a pilot team.4.
We lead these engagements and work with specialist collaborators where deeper implementation expertise is required.
Experience behind the work
Relevant proof
Siva builds software directly with AI coding tools, and the AI in the EU series documents how to run Claude Code, Cursor and Kiro with EU processing.
This describes Siva's prior professional experience, not Wolkn Minds client results. Meet Siva
Where this capability is applied: AI in Europe
Have a decision in this area?
Tell us what you are working through. We start with the context and tell you plainly if we can help.
Prefer email? hello@wolknminds.com