I work with technology companies that want to use AI to build better products, sharpen their technical strategy, and create real competitive advantage.

Many AI initiatives sound compelling in a slide deck but struggle when exposed to operational reality. The data is messy. Vessel behavior is complex. System integrations are difficult. Commercial value depends on timing, trust, adoption, and whether the output can actually support decisions in live operations.

Good AI strategy in production-heavy environments requires more than model selection. It requires judgment about what the data can support, where the operational leverage is, which product claims are credible, and how AI can become part of a robust software system.

My perspective was shaped by building AI systems where incorrect outputs carried real operational consequences. After my PhD at the Technical University of Denmark, I co-founded a company building AI for vessel optimization in live operations. Our models were deployed into production environments where errors were immediately visible and commercially costly. I stayed through acquisition, serving as CEO, CTO, and VP of AI.

That experience taught me that AI in production depends on far more than algorithms. It depends on data quality, vessel context, system architecture, user trust, evaluation discipline, and a practical understanding of how decisions are made at sea and ashore.

Since then I have worked across AI strategy, architecture, and due diligence, including:

• Assessing AI claims in industrial M&A contexts
• Building property valuation AI systems used by banks to meet Basel Committee on Banking Supervision requirements
• Architecting AI solutions for SaaS companies operating in highly regulated markets
• Designing systems where traceability, evaluation discipline, and decision reconstruction were first-class architectural concerns

My role is typically to help technology companies answer questions such as:

• Where can AI create real product advantage?
• Which AI opportunities are technically realistic?
• What data, architecture, or process gaps need to be solved first?
• How should AI features be evaluated before they reach customers?
• Which claims are strong enough to support sales, investment, or product strategy?
• What should the AI roadmap look like over the next 6 to 24 months?

What I bring:

• Experience shipping AI into live operations
• Deep understanding of how technical constraints shape business outcomes
• Practical judgment about data, models, systems, and adoption
• Creative systems thinking
• Direct, accountable collaboration

I usually work with leadership teams, product teams, and technical teams that already understand their market but want sharper AI judgment. I do not replace the internal team. I help them make better decisions, avoid expensive missteps, and turn AI from a vague ambition into a credible product and strategy advantage.

I work with only a few clients at any one time. Before starting, we should make sure there is a strong fit, because the best outcomes come from focused collaboration and honest technical judgment.