Supports technical founders in finding product-market fit
LASTMILE is a technology company which develops conversational artificial intelligence for insurance companies and banks.
The broader mission of the company is to develop tools for AI such as rasa NLU, an open-source alternative to wit.ai, api.ai or LUIS.
Example use case that is live: Trained neural net in action - A customer can change the address in her insurance contract by talking to a bot.
"SV helped us to demystify the concept of product-market-fit and iterate much quicker. Matthaus’ toolbox helped us to think much more structured about our product market fit and to choose the right experiments to validate our assumptions.
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. Eventually, this gave us enough data points to take the hard decision to stop working on our former product. Both the actual structure of the spreadsheet as well the fact that we documented everything helped us to realise that our product didn't solve a big enough problem for none of the cohorts. Additionally, it gave me as a CEO a great way to communicate structured about our progress towards PMF with my co-founder and the rest of team.
On top of his toolbox, he gave us access to data, customers 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. SV 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. 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. SV moved me very far ahead on this learning curve."