Jan Hoppe

Jan Hoppe – Product Leader and Full Stack Digital Expert

More Than a Decade of Digital Product Work – And How It Translates Into Freelance Support

Jan Hoppe is an experienced Product Leader with a rare combination of skills: He combines deep understanding of digital product development with hands-on experience in Generative AI, Performance Marketing, and building companies from the ground up. His career is characterized by the consistent connection of strategic thinking and operational excellence – from bootstrapping an AI-native startup to scaling a PropTech company from Pre-Seed to Series B, to advising DAX corporations on their digital transformation.

What makes Jan particularly valuable as a freelance consultant, coach, or fractional leader is that he doesn't just know the theory – he has lived through the challenges his clients face. Whether you're a startup founder trying to find Product-Market-Fit, a scale-up struggling with team topology, or an established company looking to integrate Generative AI into your product, Jan brings relevant, battle-tested experience to the table.

Bootstrapped Startup Experience: ganzheit – AI for Psychotherapy

Since 2025, Jan has been working as Founder and Chief Product Officer at ganzheit, a bootstrapped AI-native startup. The company develops digital tools that support psychotherapists in their therapeutic work – without disrupting the essential human connection between therapist and patient.

What makes ganzheit special is the way the startup was built from day one. As a bootstrapped company, every euro had to be turned twice. This resource scarcity led to an extremely focused product development approach, consistently aligned with the Lean Startup paradigm. The result: A founding team that successfully reached Product-Market-Fit for LLM-powered supervision for psychotherapists.

The technical architecture of ganzheit was designed as AI-native from the beginning. Concretely, this means the entire generative AI stack is deeply integrated into product development. This includes agentic coding workflows, a robust evaluation system, tracing for AI decision transparency, and thoughtful prompt management. This infrastructure enables the small team to iterate quickly while systematically improving AI output quality.

How This Experience Helps Freelance Clients:

For founders and early-stage startups, Jan understands the brutal reality of building with limited resources. He knows what it means to make every feature count, to prioritize ruthlessly, and to find creative solutions when you can't just throw money at problems. His bootstrapping experience translates directly into practical advice for teams that need to move fast without burning through their runway.

For companies exploring AI-native product development, Jan can share hard-won insights about building evaluation frameworks, implementing tracing systems, and managing prompts at scale. These aren't theoretical concepts – they're operational practices he uses daily in his own startup.

Generative AI Skills: From Early Adopter to Practitioner

Jan's experience with Generative AI isn't limited to a single project but runs as a common thread through his recent career. At Evernest, he led an early Generative AI initiative and implemented LLM-powered features that saved real estate agents an average of six hours per week. This practical experience with integrating Large Language Models into existing product landscapes gave him a deep understanding of where AI creates real value – and where it doesn't.

In his current consulting work at PRODUKTKRAFT, Jan applies this expertise in various ways. A particularly interesting project is his work on a B2B product for a generative AI sales coach. This involves not just developing a product vision and strategy in Lean Startup style, but also hands-on work on AI quality. Jan works with tracing, human-annotated evals, and LLM-as-a-judge evaluations to systematically improve alignment and performance of AI outputs. Prompt engineering isn't an afterthought here – it's an integral part of the product development process.

Another consulting project shows a different facet of AI work: Here, Jan supports a US-German company in building an LLM-enabled Databricks Data Lakehouse infrastructure. This isn't just about technical implementation, but also about the cultural shift toward data-driven decision-making – for an organization with more than 400 employees.

• How This Experience Helps Freelance Clients:**

For product teams looking to integrate AI features, Jan offers a pragmatic perspective. He's seen what works and what doesn't. He can help you avoid the common pitfall of building AI features that impress in demos but fail in production. His experience with evaluation frameworks means he can help you establish quality metrics before you ship – not after users start complaining.

For organizations building AI-first products, Jan brings operational knowledge about the entire stack: from prompt management to tracing to systematic evaluation. He can help you set up the infrastructure that allows you to iterate quickly while maintaining quality.

For companies trying to build data-informed cultures, Jan's experience with data lakehouse architecture and the organizational change management that goes with it is directly applicable. He understands that technical infrastructure is only half the battle – getting people to actually use data in their decisions is the harder part.