A de-risking expert for life science startups
I apply the lean startup framework to early-stage startups that contribute to a sustainable planet and the health of people.
About me
I'm a trained bio-scientist with 10 years of experience in life science startups. Over the past years, I've helped many founders de-risk their ideas from different perspectives (technology, market, finance), managed international R&D projects and acquired millions of Euros in public & private funding. I have co-founded a startup and was CEO of a company builder project in green biotech. Currently, I am building chemstars to bring world-class research in the chemical space to market. If you're determined to change our planet for the better, I would love to speak with you.
Startup De-Risking Strategy
Idea Validation Lean Startup
Customer Development
Venture Capital Investor Pitching
I support founders and startups with a life science background.
Life science startups are special. They typically require larger sums of capital, have longer development cycles and are much more reliant on IP than other tech startups. Yet, independent of the industry, every startup is based on assumptions that need to be true in order for the underlying business model to work. And therefore the same basic principles of de-risking apply to all startups - from biotech to chemtech, fintech, healthtech, insurtech, proptech and so on. It's the founders' job to generate data (evidence) to eliminate four types of risk, each of which can be summed up in a simple question:
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Do customers actually want your product or service (-> market risk or desirability)?
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Can you build your product or service (-> technical risk or feasibility)?
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Will you make more money on sales than you spend (-> financial risk or viability)?
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Does your product or service fit into the business environment (-> macro risk or adaptability)?
This is so obvious, it's almost painful to read, right? Yet, there are countless first-time founders, who dedicate most (if not all) of their time and energy to building a product, without ever testing that there are sufficient interested customers willing to buy from them. The good news: There are processes to create that data.
Clients &
Collaborations
