AI consulting for mid-sized companies — from strategy to working systems
I help European companies decide where AI creates real value, build systems that work, and embed them responsibly into everyday operations.
Led by Claudius Gramse — AI operator, engineer, adviser, and founder of evonomics.
Where the real work begins
Most organisations do not lack AI ideas. They struggle to choose the right ones, connect them to existing processes and data, control quality and risk, and earn adoption from the people expected to use them.
That is the gap evonomics closes: between an impressive prototype and a dependable capability the business can own, operate, and improve.
From intent to capability
Identify valuable use cases, test assumptions, set priorities, and create an AI roadmap the organisation can actually execute.
Typical work:
Design and build practical AI applications around real business processes, not demonstrations looking for a problem.
Typical work:
Put the responsibilities, controls, skills, and ways of working in place so useful systems can be deployed and scaled with confidence.
Typical work:
Experience that compounds
As Chief AI Officer at iurFRIEND, I carry operational responsibility for building AI capability inside a real business: across technology, governance, processes, and people.
Through evonomics, I help other organisations make the same difficult transition from AI ambition to responsible implementation, adapted to their own context.
In the AI Leadership Journal, I examine what changed, what matters, and what leaders should do next, without converting every product release into a revolution.
The roles are separate, but the experience reinforces itself: operational accountability keeps the advice grounded, and work across organisations sharpens the operating perspective.
Strategy with technical depth
evonomics combines business and organisational experience with hands-on AI engineering. The work ranges from architecture and prototypes to production-oriented workflows, integrations, evaluation, and operational controls.
Python, JavaScript, APIs, agent frameworks, retrieval systems, and observability tools are part of the toolkit. So are low-code and no-code platforms when they are the fastest sensible route. The technology serves the problem, not the other way round.
AI Leadership Journal
The Journal separates consequential developments from the weekly noise. It combines strategic interpretation, technical understanding, and lessons from implementation, so leaders can see what changed, why it matters, and what deserves action.
No recycled announcements. No breathless predictions. Useful analysis, written from the work.
About Claudius Gramse
I have spent more than 25 years working across technology, digital transformation, marketing, and organisational change. For the past five years, my focus has been the practical use of AI: how organisations choose it, build with it, govern it, and change the way people work around it.
Today, I work from both sides of implementation: as Chief AI Officer at iurFRIEND and, separately, as founder of evonomics. That combination gives me the technical depth to work with engineers, the operating perspective to understand organisational constraints, and the strategic experience to help leaders make defensible choices.
Move beyond the pilot
If you need to decide where AI belongs, build a system around a real process, or establish the governance and adoption needed to scale it, let’s discuss the situation.
Contact Claudius
Briefly describe the AI initiative, business process or implementation challenge. A few concrete details are enough.
