Towards a meaningful way to support technical founders in finding product-market fit for their prototypes
On top of my day job as a co-founder/CEO of dltHub I love helping technical founders succeed. Since 2015 I have been looking for a way to support founders as meaningfully as possible via Salisbury Ventures. Initially I was meeting technical founders and was helping them to iterate on their prototypes. Over time my engagement evolved. In a select few of them I became an angel investor. In two of them I became an active operational advisor, helping them 4h-6h per week.
In 2020 I have put my individual Salisbury Ventures angel checks on hold as I became a part-time Venture Partner at Angel Invest, Europe's most active angel fund. This role at Angel Invest allows me to do more what I want to do in the alloted weekly time (usually 4h/week). I'm investing 5x per year in areas and sectors that interest me. This is mostly AI, data warehouses/cloud, Open Source, dev tools. I invest in exceptional technical founder teams working on top of technology platform shifts. Just like in the early days, I usually meet the founders in the "demo stage" ahead of their product showing any product-market fit. Once invested, I coach founders to product market fit and help them raise Series A.
Case Study - From Treev to Rasa
Treev was a productivity plugin Alex and Alan worked on when I met them in their TechStars Berlin 2015 batch. Alex + Alan viewed the initial product-market fit that "Treev, similar to Dropbox, is for everyone." Together we did a user cohort analysis which identified 4 distinct user types and +10 user jobs. We then engaged in structured PMF process with over 200 user interviews across over 2 months.
The results of thie process was a deep understanding that there was no market for Treev. At the same time Alex and Alan were a founder team with an exceptional skill set and they showed great speed to iterate on future PMF. We went through a PMF iteration process three times before Alex + Alan started what became RASA. The initial RASA NLU open source library was shipped in December 2016. I stayed on as an active advisor until the Series A led by Accel in Q1 '19, dedicating 4-6 hours weekly. I am still mentoring individual Rasa managers today.
"Early on Matthaus helped us to demystify the concept of product-market-fit, iterate much quicker and get and assess feedback to our high-tech prototypes. Today, two years into running a "deep tech" startup, I see that this is very rare. There's tons of public know-how and angels for operational startups, not that much for high-tech ones. Most of my peers don't understand how valuable frequent and quick feedback is when you try to apply "deep" technologies. Before we met Matthaus, we didn't think of our users as different cohorts of people with different needs and different problems. In one of our first sessions, we setup a well structured cohort spreadsheet that allowed us to better understand why certain people used our product and why others didn't. Together, we developed new assumptions, tested them with different cohorts (e.g. product managers) and iterated every week on that. Matthaus went through this process three times with us before we started what became RASA and we shipped RASA NLU. Matthaus has been building products for "emerging" technologies and markets for over 7 years. This means, for example, that when we aimed to build and launch an open-source NLP tool vis-a-vis the big platforms, he was there for us to discuss and implement tactics - because he had done similar things before. On top of his toolbox, he gave us access to data, customers, people from his network that help us set up operations and other resources like grants that are super important as an early stage company. After meeting so many startup people through Techstars, I can assure that Matthaus is very unique with his hands-on and no-bullshit attitude. Working with him genuinely challenges us every day to go the extra mile and results in better product and processes."
"In the engineering department at Cambridge I spent years collaborating with very good people in computational physics and machine learning, and checking results carefully became second nature. When you want to make a claim in science the evidence has to be really solid, so I would always check all my parameters were converged, test for artifacts in the data, compare to baselines, etc. Matthaus taught me a leaner way to be data-driven that's key for startups. One of the most valuable things founders can do to improve is making their feedback loops faster. We now often go from idea to shipping code to customer feedback to making an important product decision within two weeks. I work on building deep learning models, often in uncharted territory, and this rhythm forces me to focus on improving the things that really matter and dropping 'nice-to-haves'. I have to be willing to make decisions with only 50% of the data I'd like to have. I just can't spend 6-9 months and hours of discussion with collaborators to come to a conclusion. I still test things systematically, but I've learned to do it fast and to see when 1-2 data points are enough to prove a point. Matthaus is a constant sounding board in this process. Also, as a CTO I have to be careful where I invest resources, because I just don't have dozens of students to try every permutation. Matthaus moved me very far ahead on this learning curve."
SV tool box - broad cohort testing - Very early on tech-intensive teams should not go for incremental changes. Instead we support you with testing various substantially different cohorts very quickly to get signs of 10x opportunities. We show you how to do this and then spend time with you doing it, week by week."
Grants & referrals to trusted accelerators such as TechStars & Y Combinators - We frequently refer companies we like. Referred startups in the past include goedle.io and trevormydata.com. We also invest ourselves and also help our portfolios to get additional grants to get the "traction" that validates your "deep" tech startup in the eyes of VCs.