Supports technical founders in finding product-market fit
LASTMILE - Next-Generation Conversational AI
LASTMILE's deep learning dialogue technology goes beyond NLP, allowing the next generation of intelligent bots and assistants to engage in in human-like, layered dialogue. This leads to more more natural conversations with higher retention and engagement. Early verticals where LASTMILE's technology is applied includes insurance companies and banks.
The broader mission of the company is to develop AI technology to make conversational AI happen. An early example of this ambition is rasa NLU, an open-source alternative to wit.ai, api.ai or LUIS.
An early example use case of a trained neural net in action that went live in '16 - A customer can change the address in her insurance contract by talking to a bot.
"Early on SV helped us to demystify the concept of product-market-fit, iterate much quicker and get and assess feedback to our prototypes. Today, two years into running a "deep tech" startup, I see that this is very rare. 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 Lastmile and we shipped Rasa. 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 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."