Impressive AI demos that fall apart in production?

Most AI projects fail not because of the technology, but because of a lack of user adoption. My background in psychology and behavioral economics allows me to design AI systems with human-centric adoption baked in from day one. By understanding mental models and reducing friction, I build tools that teams actually want to use. Combined with full-stack development experience, this ensures your AI investment delivers both technical excellence and real-world results.

Partnering with a senior specialist ensures high-trust execution and direct access to expertise without the overhead of account managers or junior developers. I build systems your team can own, prioritizing clean architecture, thorough documentation, and seamless knowledge transfer. To ensure long-term stability, I work with clients to either train their internal teams or establish clear, sustainable maintenance agreements.

While off-the-shelf tools are great for general tasks, custom AI infrastructure is required for deep integration with your tech stack, proprietary data usage, and enforcing complex business rules. I build operational systems that automate multi-step workflows—like syncing CRM data, inventory checks, and proposal routing—creating a competitive advantage that a standard chatbot cannot provide.

I bridge the gap where most AI projects fail: the translation between business requirements and technical feasibility. My background as a German Foundation of Business scholar combined with hands-on engineering means I can architect systems that are both technically sound and economically sensible. This ensures your project is aligned with your CFO’s goals while remaining highly implementable for your engineers.

I define clear success metrics—such as cost reduction, time saved, or revenue impact—before writing a single line of code. By validating assumptions with small, early experiments, we avoid the trap of 'AI for AI's sake.' If a simpler automation or spreadsheet is the better solution, I’ll tell you. My focus is on solving your business problem efficiently, not on maximizing engagement hours.

The choice depends on your specific goals: n8n is ideal for rapid prototyping and validating workflow ideas with minimal investment, while custom code (Python, Elixir/Phoenix) is reserved for high-performance, complex logic, or production-grade reliability. I select the right tool for your current stage, ensuring you optimize for speed today without sacrificing scalability tomorrow.

I prioritize production-ready engineering over hype-driven demos. By working directly with your technical teams, I ensure every implementation is robust, maintainable, and survives the scrutiny of experienced developers. My reputation is built on delivering reproducible results that add long-term value, rather than one-off prototypes that collapse after the initial phase.