Tell us a bit about you and your venture! What was your inspiration for creating Senso?
I’ll start with my inspiration. I’ve spent the last 15 years building customer experiences for banks and telcos. On my last project at a large bank, I came to the realization that there was a lack of data transparency and communication between financial service advisors and their customers. I clearly saw a broken process and a brighter future.
Senso.ai is the data communications platform for the financial services industry. Our initial pilot allows mortgage lenders and advisors to acquire, onboard, and retain profitable customers more effectively. By combining elegant user experience with machine learning, we’re building our way towards a better financial services experience, starting with the home purchase and refinance process.
Did you ever deal with contention from anyone around you (eg. family) concerning your entrepreneurial pursuits?
I consider myself one of the lucky ones. Despite many struggles, my family, friends, and community have always supported me throughout my entrepreneurial pursuit. This has been, by far, the number one thing that has kept me going throughout the years.
What was your favorite class in school?
Two in particular — art and computer science. I didn’t realize it back then, but the combination of the two would shape my ability to design and build user experiences which capture value and benefit end users.
What company or founder do you admire the most?
This is going to sound cliché, but I have to go with Elon Musk. Mainly because of his ability to think big, focus on the task at hand, and consistently follow through despite the world telling him that he ‘can’t’ time and time again.
What do you think is the most important innovation of your lifetime thus far?
During my final year at the University of Guelph, I put together a team of engineers to design and build an automated vacuum cleaner, which exceeded the capabilities of the market leader at the time. It didn’t look pretty, but it definitely scratched my entrepreneurial itch, and got my team some recognition. The only thing lacking was a startup incubator similar to NextAI, which may have been what we needed to take LazyVac to the next level.
What are 3 books, blogs or newsletters you recommend for aspiring entrepreneurs?
I enjoy reading autobiographies about relentless passion. Two books which standout are Steve Jobs and Gandhi. My favourite blog is https://waitbutwhy.com/. Tim Urban educates me and cracks me up at the same time.
What is your favorite quote on entrepreneurship?
“The two most important days in your life are the day you are born and the day you find out why.” — Mark Twain.
What do you know today that you wish you knew when you became an entrepreneur?
Most people don’t realize that a real opportunity exists in front of them until it’s too late.
My first business was in grade 9, selling custom made stickers of hip hop album covers for students to put up in their lockers. I hit a trend, but I didn’t realize it! I was just out to make some extra lunch money. If I could go back, I would tell that young hustler to seize the opportunity in front of him and go big!
When you started, what were your biggest hurdles in building your venture?
The biggest hurdle was convincing my team to put trust in me and my vision with no certainty of success, or any tangible results. I believe that the reason most early startups fail is deeply rooted in the misalignment between founder expectations. Things are always great during ‘peaks’, but true colours come out in ‘valleys’. In my experience, teams who strive in ‘valleys’ are more likely to succeed. To ensure this, early founders must stay humble, appreciate each others differences, and focus on the big picture, versus petty personal matters at a time when they own 100% of an ‘asset’ worth zero. I’m blessed to have a team that truly gets this.
What advice do you have for others looking to make an impact using AI?
As an early stage machine learning startup, collecting data is a classic ‘chicken and egg problem’. The key is to find a chicken (first dataset) which can help you produce a lot of eggs (opportunities to acquire more data)!
To do this, start by learning about what specific types of problems AI has the ability to realistically solve. Then focus on finding a problem which you’re intimately familiar with and can be solved using the latest techniques. Finally, identify what data (+ how much of it) you need, and build a product and/or the right partnerships to collect that data as quickly as possible. If you’ve done that, the snowball will start to roll down the hill.